A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases
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paper_title: GoGene: gene annotation in the fast lane

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High-throughput screens such as microarrays and RNAi screens produce huge amounts of data. They typically result in hundreds of genes, which are often further explored and clustered via enriched GeneOntology terms. The strength of such analyses is that they build on high-quality manual annotations provided with the GeneOntology. However, the weakness is that annotations are restricted to process, function and location and that they do not cover all known genes in model organisms. GoGene addresses this weakness by complementing highquality manual annotation with high-throughput text mining extracting co-occurrences of genes and ontology terms from literature. GoGene contains over 4000000 associations between genes and gene-related terms for 10 model organisms extracted from more than 18000000 PubMed entries. It does not cover only process, function and location of genes, but also biomedical categories such as diseases, compounds, techniques and mutations. By bringing it all together, GoGene provides the most recent and most complete facts about genes and can rank them according to novelty and importance. GoGene accepts keywords, gene lists, gene sequences and protein sequences as input and supports search for genes in PubMed, EntrezGene and via BLAST. Since all associations of genes to terms are supported by evidence in the literature, the results are transparent and can be verified by the user. GoGene is available at http:// gopubmed.org/gogene.
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paper_title: Bioinformatics: Recent Trends in Programs, Placements and Job Opportunities

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Amino thiol substituted dipeptides of the formula are disclosed. These compounds are useful as hypotensive agents due to their angiotensin converting enzyme inhibition activity and depending upon the definition of X may also be useful as analgesics due to their enkephalinase inhibition activity.
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paper_title: Application of text mining in the biomedical domain.

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In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for.
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paper_title: Extraction of incremental information using query evaluator

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Information Extraction is an activity of examine text for information relevant to some interest. Information extraction needs depth analysis than simple key word searches. The information extraction system recognizes and extracts knowledge from a massive literature and extracted knowledge is accumulated in a knowledge base. Many conventional automatic information extraction approaches using Natural Language Processing and Text Mining technologies have been proposed to extract meaningful information automatically in biomedical realm. These conventional approaches have considerable pitfall that whenever a different extraction goal become visible or any component in system is upgraded, extraction has to be reapplied from beginning to the whole text collection although only a minor part of the text collection might be influenced. In this paper we have applied Stanford dependency grammar to furnish easy description of the grammatical relationships in a sentence. This work relates incremental information extraction approach in which extraction needs are exhibited in the form of database queries. This work aims that in the occasion of installation of a upgraded component, reduction in the processing time takes place as compared to a conventional approach.
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paper_title: CoPub update: CoPub 5.0 a text mining system to answer biological questions

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In this article, we present CoPub 5.0, a publicly available text mining system, which uses Medline abstracts to calculate robust statistics for keyword co-occurrences. CoPub was initially developed for the analysis of microarray data, but we broadened the scope by implementing new technology and new thesauri. In CoPub 5.0, we integrated existing CoPub technology with new features, and provided a new advanced interface, which can be used to answer a variety of biological questions. CoPub 5.0 allows searching for keywords of interest and its relations to curated thesauri and provides highlighting and sorting mechanisms, using its statistics, to retrieve the most important abstracts in which the terms co-occur. It also provides a way to search for indirect relations between genes, drugs, pathways and diseases, following an ABC principle, in which A and C have no direct connection but are connected via shared B intermediates. With CoPub 5.0, it is possible to create, annotate and analyze networks using the layout and highlight options of Cytoscape web, allowing for literature based systems biology. Finally, operations of the CoPub 5.0 Web service enable to implement the CoPub technology in bioinformatics workflows. CoPub 5.0 can be accessed through the CoPub portal http://www.copub.org.
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paper_title: Literature mining for the biologist: from information retrieval to biological discovery

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For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.
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paper_title: A grid infrastructure for mixed bioinformatics data and text mining

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Summary form only given. We present a distributed infrastructure for mixed data and text mining. Our approach is based on extending the discovery net infrastructure, a grid-computing environment for knowledge discovery, to allow end users to construct complex distributed text and data mining workflows. We describe our architecture, data model and visual programming approach and present a number of mixed data text mining examples over biological data.
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paper_title: Evaluation of biomedical text-mining systems : Lessons learned from information retrieval

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Biomedical text-mining systems have great promise for improving the efficiency and productivity of biomedical researchers. However, such systems are still not in routine use. One impediment to their development is the lack of systematic and rigorous evaluation, comparable to the approaches developed for information retrieval systems. The developers of text-mining systems need to improve both test collections for system-oriented evaluation and undertake user-oriented evaluations to determine the most effective use of their systems for their intended audience.
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paper_title: TEXT AND DATA MINING FOR BIOMEDICAL DISCOVERY

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The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health-related terms to integrate consumer-friendly synonyms for medical terminologies.
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paper_title: Text Mining Functional Keywords Associated with Genes

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Modern experimental techniques provide the ability to gather vast amounts of biological data in a single experiment (e.g. DNA microarray experiment), making it extremely difficult for the researcher to interpret the data and form conclusions about the functions of the genes. Current approaches provide useful information that organizes or relates genes, but a major shortcoming is they either do not address specific functions of the genes or are constrained by functions predefined in other databases, which can be biased, incomplete, or out-of-date. We extended Andrade and Valencia’s method [1] to statistically mine functional keywords associated with genes from MEDLINE abstracts. The MEDLINE abstracts are analyzed statistically to score and rank keywords for each gene using a background set of words for baseline frequencies. We generally got very good functional keyword information about the genes we tested, which was confirmed by searching for the individual keywords in context. The keywords extracted by our algorithm reveal a wealth of potential functional concepts, which were not represented in existing public databases. We feel that this approach is general enough to apply to medical and biological literature to find other relationships: drugs vs. genes, risk-factors vs. genes, etc.
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paper_title: A UMLS-based Knowledge Acquisition Tool for Rule-based Clinical Decision Support System Development

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Decision support systems in the medical field have to be easily modified by medical experts themselves. The authors have designed a knowledge acquisition tool to facilitate the creation and maintenance of a knowledge base by the domain expert and its sharing and reuse by other institutions. The Unified Medical Language System (UMLS) contains the domain entities and constitutes the relations repository from which the expert builds, through a specific browser, the explicit domain ontology. The expert is then guided in creating the knowledge base according to the pre-established domain ontology and condition-action rule templates that are well adapted to several clinical decision-making processes. Corresponding medical logic modules are eventually generated. The application of this knowledge acquisition tool to the construction of a decision support system in blood transfusion demonstrates the value of such a pragmatic methodology for the design of rule-based clinical systems that rely on the highly progressive knowledge embedded in hospital information systems.
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paper_title: Automatic classification of biomedical texts: Experiments with a hearing loss corpus

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To obtain reliable information extraction applications has been one of the main goals of the biomedical text mining community. These applications are valuable tools to biologists in their increasingly difficult task of assimilating the knowledge contained in biomedical literature. This paper presents a novel algorithm for automatic classification of biomedical texts, specifically texts about symptoms. We apply our approach to biomedical texts related to hearing loss, obtaining promising results.
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paper_title: A text feature-based approach for literature mining of lncRNA-protein interactions

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Long non-coding RNAs (lncRNAs) play important roles in regulating transcriptional and post-transcriptional levels. Currently, Knowledge of lncRNA and protein interactions (LPIs) is crucial for biomedical researches that are related to lncRNA. Many freshly discovered LPIs are stored in biomedical literature. With over one million new biomedical journal articles published every year, just keeping up with the novel finding requires automatically extracting information by text mining. To address this issue, we apply a text feature-based text mining approach to efficiently extract LPIs from biomedical literatures. Our approach consists of four steps. By employ natural language processing (NLP) technologies, this approach extracts text features from sentences that can precisely reflect the real LPIs. Our approach involves four steps including data collection, text pre-processing, structured representation, features extraction and training model and classification. The F-score performance of our approach achieves 79.5%, and the results indicate that the proposed approach can efficiently extract LPIs from biomedical literature. The text mining approach to automatically extract lncRNA-protein interactions from literature.The efficiency of the approach is shown in related experiment and comparison studies.Extracting text features automatically from biomedical literature.
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paper_title: Searching association rules of traditional Chinese medicine on Ligusticum wallichii by text mining

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Much useful information on Ligusticum wallichii (LW) could be obtained from published literature by text mining technique. In this study, the data set on LW was downloaded from Chinese BioMedical literature database (SinoMed). Then, association rules among diseases, traditional Chinese medicine (TCM) syndromes, formulae and herbs on LW were investigated by text mining technique. These rules include TCM syndromes to diseases, formulae and combinational herbs, respectively. Diseases related with formulae including LW were mined out by executing data slicing algorithm. Finally, the results were visually demonstrated with Cytoscape 2.8 software. The main features from the mining data were: (1) LW was frequently used in treating cerebral infraction; (2) Blood stasis due to Qi deficiency was the main syndrome in TCM clinical practice; (3) Angelica sinensis was the first herb to combine with LW according to co-occurrent frequency; (4) Associated with LW, networks of TCM syndromes-diseases, formulae-diseases, TCM syndromes-formulae, and TCM syndromes-combinational herbs were constructed. These associated networks represented a holistic thinking of Chinese medicinal therapy, which might embody association rules among diseases, syndromes, formulae and herbs on LW.
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paper_title: Legal aspects of text mining

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Abstract “Text mining” covers a range of techniques that allow software to extract information from text documents. It is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are very diverse and span multiple disciplines, ranging from biomedicine to legal, business intelligence and security. From a legal perspective, text mining touches upon several areas of law, including contract law, copyright law and database law. This contribution discusses the legal issues encountered during the assembly of texts into so-called “corpora”, as well as the use of such corpora.
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paper_title: CGM: A biomedical text categorization approach using concept graph mining

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Text Categorization is used to organize and manage biomedical text databases that are growing at an exponential rate. Feature representations for documents are a crucial factor for the performance of text categorization. Most of the successful existing techniques use a vector representation based on key entities extracted from the text. In this paper we investigate a new direction where we represent a document as a graph. In this representation we identify high level concepts and build a rich graph structure that contains additional concepts and relationships. We then use graph kernel techniques to perform text categorization. The results show a significant improvement in accuracy when compared to categorization based on only the extracted concepts.
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paper_title: Using association rules mining to explore pattern of Chinese medicinal formulae (prescription) in treating and preventing breast cancer recurrence and metastasis

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BackgroundChinese herbal medicine is increasingly widely used as a complementary approach for control of breast cancer recurrence and metastasis. In this paper, we examined the implicit prescription patterns behind the Chinese medicinal formulae, so as to explore the Chinese medicinal compatibility patterns or rules in the treatment or control of breast cancer recurrence and metastasis.MethodsThis study was based on the herbs recorded in Pharmacopoeia of the People’s Republic of China, and the literature sources from Chinese Journal Net and China Master Dissertations Full-text Database (1990 – 2010) to analyze the compatibility rule of the prescription. Each Chinese herb was listed according to the selected medicinal formulae and the added information was organized to establish a database. The frequency and the association rules of the prescription patterns were analyzed using the SPSS Clenmentine Data Mining System. An initial statistical analysis was carried out to categorize the herbs according to their medicinal types and dosage, natures, flavors, channel tropism, and functions. Based on the categorization, the frequencies of occurrence were computed.ResultsThe main prescriptive features from the selected formulae of the mining data are: (1) warm or cold herbs in the Five Properties category; sweet or bitter herbs in the Five Flavors category and with affinity to the liver meridian are the most frequently prescribed in the 96 medicinal formulae; (2) herbs with tonifying and replenishing, blood-activating and stasis-resolving, spleen-strengthening and dampness-resolving or heat-clearing and detoxicating functions that are frequently prescribed; (3) herbs with blood-tonifying, yin-tonifying, spleen-strengthening and dampness-resolving, heat-clearing and detoxicating, and blood-activating with stasis-resolving functions that are interrelated and prescribed in combination with qi-tonifying herbs.ConclusionsThe results indicate that there is a close relationship between recurrence and metastasis of breast cancer with liver dysfunctions. These prescriptions focus on the herbs for nourishing the yin-blood, and emolliating and regulating the liver which seems to be the key element in the treatment process. Meanwhile, the use of qi-tonifying and spleen-strengthening herbs also forms the basis of prescription patterns.
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paper_title: Genescene: biomedical text and data mining

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To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching.
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paper_title: Biological data mining with neural networks : implementation and application of a exible decision tree extraction algorithm to genomic problem domains

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In the past, neural networks have been viewed as classification and regression systems whose internal representations were extremely difficult to interpret. It is now becoming apparent that algorithms can be designed which extract understandable representations from trained neural networks, enabling them to be used for data mining, i.e. the discovery and explanation of previously unknown relationships present in data. This paper reviews existing algorithms for extracting comprehensible representations from neural networks and describes research to generalize and extend the capabilities of one of these algorithms. The algorithm has been generalized for application to bioinformatics datasets, including the prediction of splice site junctions in Human DNA sequences. Results generated on this datasets are compared with those generated by a conventional data mining technique (C5) and conclusions drawn.
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paper_title: Overview and semantic issues of text mining

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Text mining refers to the discovery of previously unknown knowledge that can be found in text collections. In recent years, the text mining field has received great attention due to the abundance of textual data. A researcher in this area is requested to cope with issues originating from the natural language particularities. This survey discusses such semantic issues along with the approaches and methodologies proposed in the existing literature. It covers syntactic matters, tokenization concerns and it focuses on the different text representation techniques, categorisation tasks and similarity measures suggested.
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paper_title: Assessment of Latent Semantic Analysis (LSA) Text Mining Algorithms for Large Scale Mapping of Patent and Scientific Publication Documents

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In this study we conduct a thorough assessment of the LSA text mining method and its options (preprocessing, weighting, …) to grasp similarities between patent documents and scientific publications to develop a new method to detect direct science-technology linkages - as this is instrumental for research on topics in innovation management, e.g. anticommons issues. We want to assess effectiveness (in terms of precision and recall) and derive best practices on weighting and dimensionality reduction for application on patent data. We use LSA to derive similarity from a large set of patent and scientific publication documents (88,248 patent documents and 948,432 scientific publications) based on 40 similarity measurement variants (four weighting schemas are combined with ten levels of dimensionality reduction and the cosine metric). A thorough validation is set up to compare the performance of those measure variants (expert validation of 300 combinations plus a control set of 30,000 patents). We do not find evidence for the claims of LSA to be superior to plain cosine measures or simple common term or co-occurrence based measures in our data; dimensionality reduction only seems to approach cosine measures applied on the full vector space. We propose the combination of two measures based on the number of common terms (weighted by the minimum of the number of terms of both documents and weighted by the maximum of the number of terms of both documents respectively) as a more robust method to detect similarity between patents and publications.
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paper_title: Text Mining Methods and Techniques

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In recent years growth of digital data is increasing, knowledge discovery and data mining have attracted great attention with coming up need for turning such data into useful information and knowledge. The use of the information and knowledge extracted from a large amount of data benefits many applications like market analysis and business management. In many applications database stores information in text form so text mining is the one of the most resent area for research. To extract user required information is the challenging issue. Text Mining is an important step of knowledge discovery process. Text mining extracts hidden information from notstructured to semi-structured data. Text mining is the discovery by automatically extracting information from different written resources and also by computer for extracting new, previously unknown information. This survey paper tries to cover the text mining techniques and methods that solve these challenges. In this survey paper we discuss such successful techniques and methods to give effectiveness over information retrieval in text mining. The types of situations where each technology may be useful in order to help users are also discussed.
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paper_title: Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions

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We describe the basic design of a system for automatic detection of protein-protein interactions extracted from scientific abstracts. By restricting the problem domain and imposing a number of strong assumptions which include pre-speeified protein names and a limited set of verbs that represent actions, we show- that it is possible to perform accurate information extraction. The performance of tile system is evaluated with different cases of real-world interaction networks, including the Drosophila cell cycle control. The results obtained computationally are in good a~’eement with current biological knowledge and demonstrate tile feasibility of developing a flfily automated system able to describe networks of protein interactions with sufficient accuracy.
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paper_title: MedMiner: An Internet Text-Mining Tool for Biomedical Information, with Application to Gene Expression Profiling

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The trend toward high-throughput techniques in molecular biology and the explosion of online scientific data threaten to overwhelm the ability of researchers to take full advantage of available information. This problem is particularly severe in the rapidly expanding area of gene expression experiments, for example, those carried out with cDNA microarrays or oligonucleotide chips. We present an Internet-based hypertext program, MedMiner, which filters and organizes large amounts of textual and structured information returned from public search engines like GeneCards and PubMed. We demonstrate the value of the approach for the analysis of gene expression data, but MedMiner can also be extended to other areas involving molecular genetic or pharmacological information. More generally still, MedMiner can be used to organize the information returned from any arbitrary PubMed search.
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paper_title: Text-mining and information-retrieval services for molecular biology

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Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators.
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paper_title: Knowledge discovery in biology and biotechnology texts: a review of techniques, evaluation strategies, and applications.

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Arguably, the richest source of knowledge (as opposed to fact and data collections) about biology and biotechnology is captured in natural-language documents such as technical reports, conference proceedings and research articles. The automatic exploitation of this rich knowledge base for decision making, hypothesis management (generation and testing) and knowledge discovery constitutes a formidable challenge. Recently, a set of technologies collectively referred to as knowledge discovery in text (KDT) has been advocated as a promising approach to tackle this challenge. KDT comprises three main tasks: information retrieval, information extraction and text mining. These tasks are the focus of much recent scientific research and many algorithms have been developed and applied to documents and text in biology and biotechnology. This article introduces the basic concepts of KDT, provides an overview of some of these efforts in the field of bioscience and biotechnology, and presents a framework of commonly used techniques for evaluating KDT methods, tools and systems.
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paper_title: Mining clinical attributes of genomic variants through assisted literature curation in Egas

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The veritable deluge of biological data over recent years has led to the establishment of a considerable number of knowledge resources that compile curated information extracted from the literature and store it in structured form, facilitating its use and exploitation. In this article, we focus on the curation of inherited genetic variants and associated clinical attributes, such as zygosity, penetrance or inheritance mode, and describe the use of Egas for this task. Egas is a web-based platform for text-mining assisted literature curation that focuses on usability through modern design solutions and simple user interactions. Egas offers a flexible and customizable tool that allows defining the concept types and relations of interest for a given annotation task, as well as the ontologies used for normalizing each concept type. Further, annotations may be performed on raw documents or on the results of automated concept identification and relation extraction tools. Users can inspect, correct or remove automatic text-mining results, manually add new annotations, and export the results to standard formats. Egas is compatible with the most recent versions of Google Chrome, Mozilla Firefox, Internet Explorer and Safari and is available for use at https://demo.bmd-software.com/egas/Database URL: https://demo.bmd-software.com/egas/.
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paper_title: PPInterFinder—a mining tool for extracting causal relations on human proteins from literature

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One of the most common and challenging problem in biomedical text mining is to mine protein–protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder—a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. ::: ::: Database URL: http://www.biomining-bu.in/ppinterfinder/
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paper_title: A text-mining system for extracting metabolic reactions from full-text articles

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BackgroundIncreasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway — metabolic pathways — has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein–protein interactions.ResultsWhen evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task.ConclusionsWe conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.
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paper_title: RLIMS-P 2.0: A Generalizable Rule-Based Information Extraction System for Literature Mining of Protein Phosphorylation Information

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We introduce RLIMS-P version 2.0, an enhanced rule-based information extraction (IE) system for mining kinase, substrate, and phosphorylation site information from scientific literature. Consisting of natural language processing and IE modules, the system has integrated several new features, including the capability of processing full-text articles and generalizability towards different post-translational modifications (PTMs). To evaluate the system, sets of abstracts and full-text articles, containing a variety of textual expressions, were annotated. On the abstract corpus, the system achieved F-scores of 0.91, 0.92, and 0.95 for kinases, substrates, and sites, respectively. The corresponding scores on the full-text corpus were 0.88, 0.91, and 0.92. It was additionally evaluated on the corpus of the 2013 BioNLP-ST GE task, and achieved an F-score of 0.87 for the phosphorylation core task, improving upon the results previously reported on the corpus. Full-scale processing of all abstracts in MEDLINE and all articles in PubMed Central Open Access Subset has demonstrated scalability for mining rich information in literature, enabling its adoption for biocuration and for knowledge discovery. The new system is generalizable and it will be adapted to tackle other major PTM types. RLIMS-P 2.0 online system is available online (http://proteininformationresource.org/rlimsp/) and the developed corpora are available from iProLINK (http://proteininformationresource.org/iprolink/).
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paper_title: Search terms and a validated brief search filter to retrieve publications on health-related values in Medline: a word frequency analysis study

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Objective Healthcare debates and policy developments are increasingly concerned with a broad range of values-related areas. These include not only ethical, moral, religious, and other types of values ‘proper’, but also beliefs, preferences, experiences, choices, satisfaction, quality of life, etc. Research on such issues may be difficult to retrieve. This study used word frequency analysis to generate a broad pool of search terms and a brief filter to facilitate relevant searches in bibliographic databases. ::: ::: Methods Word frequency analysis for ‘values terms’ was performed on citations on diabetes, obesity, dementia, and schizophrenia (Medline; 2004–2006; 4440 citations; 1 110 291 words). Concordance® and SPSS 14.0 were used. Text words and MeSH terms of high frequency and precision were compiled into a search filter. It was validated on datasets of citations on dentistry and food hypersensitivity. ::: ::: Results 144 unique text words and 124 unique MeSH terms of moderate and high frequency (≥20) and very high precision (≥90%) were identified. Of these, 19 text words and seven MeSH terms were compiled into a ‘brief values filter’. In the derivation dataset, it had a sensitivity of 76.8% and precision of 86.8%. In the validation datasets, its sensitivity and precision were, respectively, 70.1% and 63.6% (food hypersensitivity) and 47.1% and 82.6% (dentistry). ::: ::: Conclusions This study provided a varied pool of search terms and a simple and highly effective tool for retrieving publications on health-related values. Further work is required to facilitate access to such research and enhance its chances of being translated into practice, policy, and service improvements.
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paper_title: BANNER: An Executable Survey of Advances in Biomedical Named Entity Recognition

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There has been an increasing amount of research on biomedical named entity recognition, t he most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text. BANNER is available for download at http://banner.sourceforge.net.
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paper_title: Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature

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Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains ‘locked’ in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3 % for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10 % in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5 %. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.
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paper_title: HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways

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The knowledge on protein–protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer’s disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks.
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paper_title: Improving information retrieval using Medical Subject Headings Concepts: a test case on rare and chronic diseases.

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BACKGROUND: As more scientific work is published, it is important to improve access to the biomedical literature. Since 2000, when Medical Subject Headings (MeSH) Concepts were introduced, the MeSH Thesaurus has been concept based. Nevertheless, information retrieval is still performed at the MeSH Descriptor or Supplementary Concept level. OBJECTIVE: The study assesses the benefit of using MeSH Concepts for indexing and information retrieval. METHODS: Three sets of queries were built for thirty-two rare diseases and twenty-two chronic diseases: (1) using PubMed Automatic Term Mapping (ATM), (2) using Catalog and Index of French-language Health Internet (CISMeF) ATM, and (3) extrapolating the MEDLINE citations that should be indexed with a MeSH Concept. RESULTS: Type 3 queries retrieve significantly fewer results than type 1 or type 2 queries (about 18,000 citations versus 200,000 for rare diseases; about 300,000 citations versus 2,000,000 for chronic diseases). CISMeF ATM also provides better precision than PubMed ATM for both disease categories. DISCUSSION: Using MeSH Concept indexing instead of ATM is theoretically possible to improve retrieval performance with the current indexing policy. However, using MeSH Concept information retrieval and indexing rules would be a fundamentally better approach. These modifications have already been implemented in the CISMeF search engine.
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paper_title: A hybrid named entity tagger for tagging human proteins/genes

paper_content:

The predominant step and pre-requisite in the analysis of scientific literature is the extraction of gene/protein names in biomedical texts. Though many taggers are available for this Named Entity Recognition (NER) task, we found none of them achieve a good state-of-art tagging for human genes/proteins. As most of the current text mining research is related to human literature, a good tagger to precisely tag human genes and proteins is highly desirable. In this paper, we propose a new hybrid approach based on (a) machine learning algorithm (conditional random fields), (b) set of (manually constructed) rules, and (c) a novel abbreviation identification algorithm to surmount the common errors observed in available taggers to tag human genes/proteins. Experiment results on JNLPBA2004 corpus show that our domain specific approach achieves a high precision of 80.47, F-score of 75.77 and outperforms most of the state-of-the-art systems. However, the recall of 71.60 still remains low and leaves much room for future improvement.
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paper_title: Text mining in radiology reports by statistical machine translation approach

paper_content:

Medical text mining has gained increasing popularity in recent years. Now a days, large amount of medical text data are daily generated in health institutions, but never refer again as it is very time consuming task. In Radiology domain, most of the reports are in free text format and usually unprocessed, hence it is difficult to access the valuable information for medical professional unless proper text mining is not applied. There are some systems existing for radiology report information retrieval like MedLEE, NeuRadIR, CBIR but very few of them make use of text associated with image. This paper proposes a text mining system to deals with this problem by using statistical machine translation approach. The System stores the text and image features to find the match report. The SVM classifier is use in SMT approach to check whether entered report present in database or not. The system will return the similar report match with the entered report from the database.
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paper_title: Selecting an Ontology for Biomedical Text Mining

paper_content:

Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documentation of the qualities of the ontologies. A number of methodologies for evaluating ontologies have been developed, but it is difficult for users by using these methods to select an ontology. In this paper, we propose a framework for selecting the most appropriate ontology for a particular text mining application. The framework comprises three components, each of which considers different aspects of requirements of text mining applications on ontologies. We also present an experiment based on the framework choosing an ontology for a gene normalization system.
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paper_title: Bayesian information extraction network for Medline abstract

paper_content:

Biomedical is a huge domain that combines a variety of research areas. MEDELINE is one of the largest biomedical databases. Thereby, the searching of pertinent information through Medline has become a difficult task. That's why; we need to develop information extraction systems in order to facilitate the treatment and the representation of data according to the user's need. This paper applies Bayesian Networks to support information extraction based on ontological annotation from Medline. We present a tool developed that combines between semantic and probabilistic reasoning techniques.
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paper_title: Mining and modeling linkage information from citation context for improving biomedical literature retrieval

paper_content:

Mining linkage information from the citation graph has been shown to be effective in identifying important literatures. However, the question of how to utilize linkage information from the citation graph to facilitate literature retrieval still remains largely unanswered. In this paper, given the context of biomedical literature retrieval, we first conduct a case study in order to find out whether applying PageRank and HITS algorithms directly to the citation graph is the best way of utilizing citation linkage information for improving biomedical literature retrieval. Second, we propose a probabilistic combination framework for integrating citation information into the content-based information retrieval weighting model. Based on the observations of the case study, we present two strategies for modeling the linkage information contained in the citation graph. The proposed framework provides a theoretical support for the combination of content and linkage information. Under this framework, exhaustive parameter tuning can be avoided. Extensive experiments on three TREC Genomics collections demonstrate the advantages and effectiveness of our proposed methods.
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paper_title: Towards Extracting Supporting Information About Predicted Protein-Protein Interactions

paper_content:

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.
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paper_title: Knowledge based word-concept model estimation and refinement for biomedical text mining

paper_content:

Display Omitted We describe a method to generate word-concept statistical models from a knowledge baseThis method integrates knowledge base descriptions and corpora informationWord sense disambiguation with this method is better than state-of-the-art approachesRanking of citation with the model improves the performance of baseline approaches Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation.In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences.The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches.
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paper_title: Mining Gene-centric Relationships from Literature to Support Drug Discovery

paper_content:

Identifying drug target candidates is an important task for early development throughout the drug discovery process. This process is supported by the development of new high-throughput technologies that enable better understanding of disease mechanism. With the push for personalized medicine, more experimental data are produced to identify how the genetics differ among individuals with respect to disease mechanism and drug response. It becomes critical to facilitate effective analysis of the large amount of biological data. In this paper, we describe our solution in employing text mining as a technique for finding scientific information for target and biomarker discovery from the biomedical literature. Additionally, we discuss how the extracted knowledge can be an effective resource for the analysis of biological data such as next-generation sequencing data.
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paper_title: Quantifying the Impact and Extent of Undocumented Biomedical Synonymy

paper_content:

Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendium of biomedical thesauri. First, we demonstrate that missing synonymy has a significant negative impact on named entity normalization, an important problem within the field of biomedical text mining. To estimate the amount synonymy currently missing from thesauri, we develop a probabilistic model for the construction of synonym terminologies that is capable of handling a wide range of potential biases, and we evaluate its performance using the broader domain of near-synonymy among general English words. Our model predicts that over 90% of these relationships are currently undocumented, a result that we support experimentally through "crowd-sourcing." Finally, we apply our model to biomedical terminologies and predict that they are missing the vast majority (>90%) of the synonymous relationships they intend to document. Overall, our results expose the dramatic incompleteness of current biomedical thesauri and suggest the need for "next-generation," high-coverage lexical terminologies.
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paper_title: Concepts extraction for medical documents using ontology

paper_content:

In the biomedical domain large amount of text documents are unstructured information is available in digital text form. Text Mining is the method or technique to find for interesting and useful information from unstructured text. Text Mining is also an important task in medical domain. The technique uses for Information retrieval, Information extraction and natural language processing (NLP). Traditional approaches for information retrieval are based on key based similarity. These approaches are used to overcome these problems; Semantic text mining is to discover the hidden information from unstructured text and making relationships of the terms occurring in them. In the biomedical text, the text should be in the form of text which can be present in the books, articles, literature abstracts, and so forth. Most of information is stored in the text format, so in this paper we will focus on the role of ontology for semantic text mining by using WordNet. Specifically, we have presented a model for extracting concepts from text documents using linguistic ontology in the domain of medical.
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paper_title: Extraction of drug-disease relations from MEDLINE abstracts

paper_content:

Biological research literature, as in many other domains of human activity, is a rich source of knowledge. MEDLINE is a huge database of biomedical information and life sciences; it provides information in the form of abstracts and documents. However, extracting this information leads to various problems, related to the types of information such as recognition of all terms related to the domain of texts, concepts associated with them, as well as identifying the types of relationships. In this context, we suggest in this paper an approach to extract disease-drug relations: in a first step, we employ Natural Language Processing techniques for the abstracts' preprocessing. In a second step we extract a set of features from the preprocessed abstracts. Finally we extract a disease-drug relation using machine learning classifier.
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paper_title: Text Mining for Bioinformatics: State of the Art Review

paper_content:

Biomedical literatures have been increased at the exponential rate. To find the useful and needed information from such a huge data set is a daunting task for users. Text mining is a powerful tool to solve this problem. In this paper, we surveyed on text mining in Bioinformatics with emphasis on applications of text mining for bioinformatics. In this paper, the main research directions of text mining in bioinformatics are accompanied with detailed examples. This paper suited the need for the state-of-the-art of the field of text mining in Bioinformatics because of the rapid development in both text mining and bioinformatics. Finally, the problems and future way are identified at last.
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paper_title: Text mining describes the use of statistical and epidemiological methods in published medical research

paper_content:

Abstract Objective To describe trends in the use of statistical and epidemiological methods in the medical literature over the past 2 decades. Study Design and Setting We obtained all 1,028,786 articles from the PubMed Central Open-Access archive (retrieved May 9, 2015). We focused on 113,450 medical research articles. A Delphi panel identified 177 statistical/epidemiological methods pertinent to clinical researchers. We used a text-mining approach to determine if a specific statistical/epidemiological method was encountered in a given article. We report the proportion of articles using a specific method for the entire cross-sectional sample and also stratified into three blocks of time (1995–2005; 2006–2010; 2011–2015). Results Numeric descriptive statistics were commonplace (96.4% articles). Other frequently encountered methods groups included statistical inferential concepts (52.9% articles), epidemiological measures of association (53.5% articles) methods for diagnostic/classification accuracy (40.1% articles), hypothesis testing (28.8% articles), ANOVA (23.2% articles), and regression (22.6% articles). We observed relative percent increases in the use of: regression (103.0%), missing data methods (217.9%), survival analysis (147.6%), and correlated data analysis (192.2%). Conclusions This study identified commonly encountered and emergent methods used to investigate medical research problems. Clinical researchers must be aware of the methodological landscape in their field, as statistical/epidemiological methods underpin research claims.
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paper_title: Mapping Gene/Protein Names in Free Text to Biomedical Databases

paper_content:

Observing that many biomedical databases have been developed and maintained independently, their records referring to the same entities may have different sets of synonyms. Integration of names pertaining to the same entity would provide a more comprehensive list of synonyms than each individual database. We have assembled BioThesaurus, a thesaurus of proteins and their corresponding genes compiled from multiple databases for all UniProtKB records. In this study, the coverage of BioThesaurus, and the contribution of each individual database were assessed for several organisms. The result indicates that the coverage of BioThesaurus is over 80% for most of the organisms with an average of 85.4%. When restricted to individual databases or resources, the percentages dropped ranging from 3 to 30%. The study demonstrated that each individual database or resource has some synonyms not covered by other databases or resources, and a list of names compiled from multiple databases would be desired for systems requiring high recall.
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paper_title: Intelligent Agent System for Bio-medical Literature Mining

paper_content:

With the advances of World Wide Web technology and advanced research in bioinformatics and systems biology domain has highlighted the increasing need for automatic information extraction [IE] system to extract information from scientific literature databases. Extraction of scientific information in biomedical articles is a central task for supporting biomarker discovery efforts. In this paper, we propose an algorithm that is capable of extracting scientific information on biomarker like gene, genome, disease, allele, cell etc from the text by finding out the focal topic of the document and extracting the most relevant properties of that topic. The topic and its properties are represented as semantic networks and then stored in a database. This IE algorithm will extract the most important biological terms and relation by statistical and pattern matching NLP techniques. This IE tool expected to help the researchers to get the latest information on biomarker discovery and its other biomedical research advances. We show preliminary results, demonstrating that the method has a strong potential to biomarker discovery methods.
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paper_title: Identifying gene-disease associations using word proximity and similarity of Gene Ontology terms

paper_content:

Associating genes with diseases is an active area of research because it is useful for helping human health with applications to clinical diagnosis and therapy. This paper proposes two methods to guide the associations between genes and diseases: (1) making use of the proximity relationship between genes and diseases and (2) utilizing GO terms shared by genes and diseases for similarity comparison. The experiments show that associations utilizing GO terms perform better than using word proximity. The results reveal that the GO terms act as a good gene-disease association feature.
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paper_title: Automatic classification of biomedical texts: Experiments with a hearing loss corpus

paper_content:

To obtain reliable information extraction applications has been one of the main goals of the biomedical text mining community. These applications are valuable tools to biologists in their increasingly difficult task of assimilating the knowledge contained in biomedical literature. This paper presents a novel algorithm for automatic classification of biomedical texts, specifically texts about symptoms. We apply our approach to biomedical texts related to hearing loss, obtaining promising results.
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paper_title: Biomedical text mining for concept identification from traditional medicine literature

paper_content:

In recent years, vast amount of biomedical literature is produced and published. Recent developments in biomedical text mining shows potential for supporting scientists in understanding new information from the existing biomedical literature because volume of electronically available biomedical literature are increasing massively. Automated literature mining offers one opportunity to discover different entities from literature. Web Technologies allow these entities to be stores and publish in the form to the further reuse by the researchers. The approach presented here includes text mining methodologies to automatically extract different entities from biomedical text. For this purpose biomedical articles based on Traditional Chinese medicine are extracted from Bio Med Central and Pub Med Central and used as corpus. Using text mining techniques of tokenization, splitting, stemming, lemmatization, parsing, named entity recognition are used for preprocessing of corpus. Candidate terms are identified by applying C-Value algorithm. These candidate terms and existing Seed/Ontological Terms are tagged in corpus. Using lexical and contextual profiles comparison between candidate terms and already existed Seed/Ontological Terms, we have identified new concepts. Identified concepts are evaluated.
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paper_title: Mining biomedical data from hypertext documents

paper_content:

Data mining is a process of discovering useful information from a database and analysis of extracted information. Text mining uses many techniques of data mining. It primarily deals with unstructured data. Web mining is an extension of text mining since it deals with unstructured data. Data mining relates to find data from “static databases” which contains “structured” data where as, web mining plays with data that are “dynamic” and “unstructured”. In this papers our goal is to mine biomedical data from hypertext documents (e.g., mining data from web contents) using text mining techniques with the help of “biomedical ontology”. Web data repositories are the hypertext documents. Texts in the Hypertext documents are unstructured and they contain Hypertext Markup Language (HTML) tags, scripting languages, images, audios, videos, URLs etc. We collect a number of documents using Google crawler and preprocess the hypertext documents and extract the text data. Next, we identify whether a word is a biomedical entity or not by using a biomedical database the “Unified Medical Language System (UMLS) metathesaurus”. The mapping of biomedical entity from the metathesaurus will be done based on keyword query. Then we apply the result to re-rank the web documents to find most relevant documents. We conclude that the more occurrence of a biomedical entity in a page, the more relevant the page is, and thus, we can re-rank the documents to find the most relevant documents by using text mining technique.
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paper_title: The Impact of Directionality in Predications on Text Mining

paper_content:

The number of publications in biomedicine is increasing enormously each year. To help researchers digest the information in these documents, text mining tools are being developed that present co-occurrence relations between concepts. Statistical measures are used to mine interesting subsets of relations. We demonstrate how directionality of these relations affects interestingness. Support and confidence, simple data mining statistics, are used as proxies for interestingness metrics. We first built a test bed of 126,404 directional relations extracted from biomedical abstracts, which we represent as graphs containing a central starting concept and 2 rings of associated relations. We manipulated directionality in four ways and randomly selected 100 starting concepts as a test sample for each graph type. Finally, we calculated the number of relations and their support and confidence. Variation in directionality significantly affected the number of relations as well as the support and confidence of the four graph types.
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paper_title: Disease-Disease Relationships for Rheumatic Diseases: Web-Based Biomedical Textmining an Knowledge Discovery to Assist Medical Decision Making

paper_content:

The MEDLINE database (Medical Literature Analysis and Retrieval System Online) contains an enormously increasing volume of biomedical articles. There is urgent need for techniques which enable the discovery, the extraction, the integration and the use of hidden knowledge in those articles. Text mining aims at developing technologies to help cope with the interpretation of these large volumes of publications. Co-occurrence analysis is a technique applied in text mining and the methodologies and statistical models are used to evaluate the significance of the relationship between entities such as disease names, drug names, and keywords in titles, abstracts or even entire publications. In this paper we present a method and an evaluation on knowledge discovery of disease-disease relationships for rheumatic diseases. This has huge medical relevance, since rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. In this study, we interviewed medical experts and searched the ACR (American College of Rheumatology) web site in order to select the most observed rheumatic diseases to explore disease-disease relationships. We used a web based text-mining tool to find disease names and their co-occurrence frequencies in MEDLINE articles for each disease. After finding disease names and frequencies, we normalized the names by interviewing medical experts and by utilizing biomedical resources. Frequencies are normally a good indicator of the relevance of a concept but they tend to overestimate the importance of common concepts. We also used Pointwise Mutual Information (PMI) measure to discover the strength of a relationship. PMI provides an indication of how more often the query and concept co-occur than expected by change. After finding PMI values for each disease, we ranked these values and frequencies together. The results reveal hidden knowledge in articles regarding rheumatic diseases indexed by MEDLINE, thereby exposing relationships that can provide important additional information for medical experts and researchers for medical decision-making.
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paper_title: A Framework for Semisupervised Feature Generation and Its Applications in Biomedical Literature Mining

paper_content:

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is: EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the performance of these low-frequency features can be greatly boosted and new information from unlabeled can be incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER), protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets.
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paper_title: A Verb-Centric Approach for Relationship Extraction in Biomedical Text

paper_content:

Advances in biomedical technology and research have resulted in a large number of research findings, which are primarily published in unstructured text such as journal articles. Text mining techniques have been thus employed to extract knowledge from such data. In this article we focus on the task of identifying and extracting relations between bio-entities such as green tea and breast cancer. Unlike previous work that employs heuristics such as co-occurrence patterns and handcrafted syntactic rules, we propose a verb-centric algorithm. This algorithm identifies and extracts the main verb(s) in a sentence, therefore, it does not require the usage of predefined rules or patterns. Using the main verb(s) it then extracts the two involved entities of a relationship. The biomedical entities are identified using a dependence parse tree by applying syntactic and linguistic features such as preposition phrases and semantic role analysis. The proposed verb-centric approach can effectively handle complex sentence structures such as clauses and conjunctive sentences. We evaluate the algorithm on several data sets and achieve an average F-score of 0.905, which is significantly higher than that of previous work.
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paper_title: A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora

paper_content:

A number of techniques such as information extraction, document classification, document clustering and information visualization have been developed to ease extraction and understanding of information embedded within text documents. However, knowledge that is embedded in natural language texts is difficult to extract using simple pattern matching techniques and most of these methods do not help users directly understand key concepts and their semantic relationships in document corpora, which are critical for capturing their conceptual structures. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured texts that computers can not interpret very easily. In this paper, we have presented a novel Biomedical Knowledge Extraction and Visualization framework, BioKEVis to identify key information components from biomedical text documents. The information components are centered on key concepts. BioKEVis applies linguistic analysis and Latent Semantic Analysis (LSA) to identify key concepts. The information component extraction principle is based on natural language processing techniques and semantic-based analysis. The system is also integrated with a biomedical named entity recognizer, ABNER, to tag genes, proteins and other entity names in the text. We have also presented a method for collating information extracted from multiple sources to generate semantic network. The network provides distinct user perspectives and allows navigation over documents with similar information components and is also used to provide a comprehensive view of the collection. The system stores the extracted information components in a structured repository which is integrated with a query-processing module to handle biomedical queries over text documents. We have also proposed a document ranking mechanism to present retrieved documents in order of their relevance to the user query.
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paper_title: Reconstructing transcriptional Regulatory Networks using data integration and Text Mining

paper_content:

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.
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paper_title: A Survey on Chemical Text Mining Techniques for Identifying Relationship Network between Drug Disease Genes and Molecules

paper_content:

Text mining plays essential roles in the field of Chemoinformatics to reveal unknown information. The enormous amount of biomedical information is available on internet and resides in the form of published articles, files, patents etc. As the rich source of data is growing massively, it is widely contributing to the scientific researchers. Text mining is the most widely used in field of Natural Language Processing. The Text Pre-processing and data analysis techniques applied on biomedical literature allows us to identify ad investigate new theories. Finding the association between the chemical entities like drug, disease, genes and molecules is the new area of focus for researchers. This paper presents the study on several approaches and techniques proposed for chemical text-mining to identify relationship network for drug-disease, disease-gene associations. In this paper, we focus on comparative analysis of various Text mining techniques used for chemical literature with their results evaluations as well as observations.
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paper_title: High-Performance Biomedical Association Mining with MapReduce

paper_content:

MapReduce has been applied to data-intensive applications in different domains because of its simplicity, scalability and fault-tolerance. However, its uses in biomedical association mining are still very limited. In this paper, we investigate using MapReduce to efficiently mine the associations between biomedical terms extracted from a set of biomedical articles. First, biomedical terms were obtained by matching text to Unified Medical Language System (UMLS) Metathesaurus, a biomedical vocabulary and standard database. Then we developed a MapReduce algorithm that could be used to calculate a category of interestingness measures defined on the basis of a 2x2 contingency table. This algorithm consists of two MapReduce jobs and takes a stripes approach to reduce the number of intermediate results. Experiments were conducted using Amazon Elastic MapReduce (EMR) with an input of 3610 articles retrieved from two biomedical journals. Test results indicate that our algorithm has linear scalability.
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paper_title: Collaborative semi-automatic annotation of the biomedical literature

paper_content:

The increasing availability of whole human genomes due to the improvements in high-throughput sequencing technologies makes the interpretation and annotation of data at a whole-genome scale more and more feasible. However, these tasks critically depend on the availability of knowledge already stored in databases or published in the scientific literature. This scenario requires new reliable and integrative information extraction systems to be available to the biomedical community. In this work we present a hybrid approach for mining the wealth of knowledge stored in the scientific literature. This approach is based on the use of efficient text-mining tools in combination with highly accurate collaborative human curation. BioNotate-2.0 is an open source, modular friendly tool which implements a collaborative semi-automatic annotation platform in which human and automated annotations are efficiently combined. BioNotate-2.0 also builds upon the Semantic Web, facilitating the dissemination of annotated facts into other resources and pipelines. BioNotate-2.0 allows any interested user to run his own annotation efforts or to contribute to an existing annotation project. To access and contribute to the BioNotate-2.0 annotation platform, please check: http://genome2.ugr.es/bionotate2/. BioNotate source code is available at: http://sourceforge.net/projects/bionotate/.
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paper_title: Mapping Gene/Protein Names in Free Text to Biomedical Databases

paper_content:

Observing that many biomedical databases have been developed and maintained independently, their records referring to the same entities may have different sets of synonyms. Integration of names pertaining to the same entity would provide a more comprehensive list of synonyms than each individual database. We have assembled BioThesaurus, a thesaurus of proteins and their corresponding genes compiled from multiple databases for all UniProtKB records. In this study, the coverage of BioThesaurus, and the contribution of each individual database were assessed for several organisms. The result indicates that the coverage of BioThesaurus is over 80% for most of the organisms with an average of 85.4%. When restricted to individual databases or resources, the percentages dropped ranging from 3 to 30%. The study demonstrated that each individual database or resource has some synonyms not covered by other databases or resources, and a list of names compiled from multiple databases would be desired for systems requiring high recall.
---
paper_title: Selecting an Ontology for Biomedical Text Mining

paper_content:

Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documentation of the qualities of the ontologies. A number of methodologies for evaluating ontologies have been developed, but it is difficult for users by using these methods to select an ontology. In this paper, we propose a framework for selecting the most appropriate ontology for a particular text mining application. The framework comprises three components, each of which considers different aspects of requirements of text mining applications on ontologies. We also present an experiment based on the framework choosing an ontology for a gene normalization system.
---
paper_title: Intelligent Agent System for Bio-medical Literature Mining

paper_content:

With the advances of World Wide Web technology and advanced research in bioinformatics and systems biology domain has highlighted the increasing need for automatic information extraction [IE] system to extract information from scientific literature databases. Extraction of scientific information in biomedical articles is a central task for supporting biomarker discovery efforts. In this paper, we propose an algorithm that is capable of extracting scientific information on biomarker like gene, genome, disease, allele, cell etc from the text by finding out the focal topic of the document and extracting the most relevant properties of that topic. The topic and its properties are represented as semantic networks and then stored in a database. This IE algorithm will extract the most important biological terms and relation by statistical and pattern matching NLP techniques. This IE tool expected to help the researchers to get the latest information on biomarker discovery and its other biomedical research advances. We show preliminary results, demonstrating that the method has a strong potential to biomarker discovery methods.
---
paper_title: Disease-Disease Relationships for Rheumatic Diseases: Web-Based Biomedical Textmining an Knowledge Discovery to Assist Medical Decision Making

paper_content:

The MEDLINE database (Medical Literature Analysis and Retrieval System Online) contains an enormously increasing volume of biomedical articles. There is urgent need for techniques which enable the discovery, the extraction, the integration and the use of hidden knowledge in those articles. Text mining aims at developing technologies to help cope with the interpretation of these large volumes of publications. Co-occurrence analysis is a technique applied in text mining and the methodologies and statistical models are used to evaluate the significance of the relationship between entities such as disease names, drug names, and keywords in titles, abstracts or even entire publications. In this paper we present a method and an evaluation on knowledge discovery of disease-disease relationships for rheumatic diseases. This has huge medical relevance, since rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. In this study, we interviewed medical experts and searched the ACR (American College of Rheumatology) web site in order to select the most observed rheumatic diseases to explore disease-disease relationships. We used a web based text-mining tool to find disease names and their co-occurrence frequencies in MEDLINE articles for each disease. After finding disease names and frequencies, we normalized the names by interviewing medical experts and by utilizing biomedical resources. Frequencies are normally a good indicator of the relevance of a concept but they tend to overestimate the importance of common concepts. We also used Pointwise Mutual Information (PMI) measure to discover the strength of a relationship. PMI provides an indication of how more often the query and concept co-occur than expected by change. After finding PMI values for each disease, we ranked these values and frequencies together. The results reveal hidden knowledge in articles regarding rheumatic diseases indexed by MEDLINE, thereby exposing relationships that can provide important additional information for medical experts and researchers for medical decision-making.
---
paper_title: A Framework for Semisupervised Feature Generation and Its Applications in Biomedical Literature Mining

paper_content:

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is: EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the performance of these low-frequency features can be greatly boosted and new information from unlabeled can be incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER), protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets.
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paper_title: Event extraction with complex event classification using rich features.

paper_content:

Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge.
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paper_title: Identifying gene-disease associations using word proximity and similarity of Gene Ontology terms

paper_content:

Associating genes with diseases is an active area of research because it is useful for helping human health with applications to clinical diagnosis and therapy. This paper proposes two methods to guide the associations between genes and diseases: (1) making use of the proximity relationship between genes and diseases and (2) utilizing GO terms shared by genes and diseases for similarity comparison. The experiments show that associations utilizing GO terms perform better than using word proximity. The results reveal that the GO terms act as a good gene-disease association feature.
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paper_title: Automatic classification of biomedical texts: Experiments with a hearing loss corpus

paper_content:

To obtain reliable information extraction applications has been one of the main goals of the biomedical text mining community. These applications are valuable tools to biologists in their increasingly difficult task of assimilating the knowledge contained in biomedical literature. This paper presents a novel algorithm for automatic classification of biomedical texts, specifically texts about symptoms. We apply our approach to biomedical texts related to hearing loss, obtaining promising results.
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paper_title: Biomedical text mining for concept identification from traditional medicine literature

paper_content:

In recent years, vast amount of biomedical literature is produced and published. Recent developments in biomedical text mining shows potential for supporting scientists in understanding new information from the existing biomedical literature because volume of electronically available biomedical literature are increasing massively. Automated literature mining offers one opportunity to discover different entities from literature. Web Technologies allow these entities to be stores and publish in the form to the further reuse by the researchers. The approach presented here includes text mining methodologies to automatically extract different entities from biomedical text. For this purpose biomedical articles based on Traditional Chinese medicine are extracted from Bio Med Central and Pub Med Central and used as corpus. Using text mining techniques of tokenization, splitting, stemming, lemmatization, parsing, named entity recognition are used for preprocessing of corpus. Candidate terms are identified by applying C-Value algorithm. These candidate terms and existing Seed/Ontological Terms are tagged in corpus. Using lexical and contextual profiles comparison between candidate terms and already existed Seed/Ontological Terms, we have identified new concepts. Identified concepts are evaluated.
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paper_title: Mining biomedical data from hypertext documents

paper_content:

Data mining is a process of discovering useful information from a database and analysis of extracted information. Text mining uses many techniques of data mining. It primarily deals with unstructured data. Web mining is an extension of text mining since it deals with unstructured data. Data mining relates to find data from “static databases” which contains “structured” data where as, web mining plays with data that are “dynamic” and “unstructured”. In this papers our goal is to mine biomedical data from hypertext documents (e.g., mining data from web contents) using text mining techniques with the help of “biomedical ontology”. Web data repositories are the hypertext documents. Texts in the Hypertext documents are unstructured and they contain Hypertext Markup Language (HTML) tags, scripting languages, images, audios, videos, URLs etc. We collect a number of documents using Google crawler and preprocess the hypertext documents and extract the text data. Next, we identify whether a word is a biomedical entity or not by using a biomedical database the “Unified Medical Language System (UMLS) metathesaurus”. The mapping of biomedical entity from the metathesaurus will be done based on keyword query. Then we apply the result to re-rank the web documents to find most relevant documents. We conclude that the more occurrence of a biomedical entity in a page, the more relevant the page is, and thus, we can re-rank the documents to find the most relevant documents by using text mining technique.
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paper_title: A Verb-Centric Approach for Relationship Extraction in Biomedical Text

paper_content:

Advances in biomedical technology and research have resulted in a large number of research findings, which are primarily published in unstructured text such as journal articles. Text mining techniques have been thus employed to extract knowledge from such data. In this article we focus on the task of identifying and extracting relations between bio-entities such as green tea and breast cancer. Unlike previous work that employs heuristics such as co-occurrence patterns and handcrafted syntactic rules, we propose a verb-centric algorithm. This algorithm identifies and extracts the main verb(s) in a sentence, therefore, it does not require the usage of predefined rules or patterns. Using the main verb(s) it then extracts the two involved entities of a relationship. The biomedical entities are identified using a dependence parse tree by applying syntactic and linguistic features such as preposition phrases and semantic role analysis. The proposed verb-centric approach can effectively handle complex sentence structures such as clauses and conjunctive sentences. We evaluate the algorithm on several data sets and achieve an average F-score of 0.905, which is significantly higher than that of previous work.
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paper_title: Mining and modeling linkage information from citation context for improving biomedical literature retrieval

paper_content:

Mining linkage information from the citation graph has been shown to be effective in identifying important literatures. However, the question of how to utilize linkage information from the citation graph to facilitate literature retrieval still remains largely unanswered. In this paper, given the context of biomedical literature retrieval, we first conduct a case study in order to find out whether applying PageRank and HITS algorithms directly to the citation graph is the best way of utilizing citation linkage information for improving biomedical literature retrieval. Second, we propose a probabilistic combination framework for integrating citation information into the content-based information retrieval weighting model. Based on the observations of the case study, we present two strategies for modeling the linkage information contained in the citation graph. The proposed framework provides a theoretical support for the combination of content and linkage information. Under this framework, exhaustive parameter tuning can be avoided. Extensive experiments on three TREC Genomics collections demonstrate the advantages and effectiveness of our proposed methods.
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paper_title: High-Performance Biomedical Association Mining with MapReduce

paper_content:

MapReduce has been applied to data-intensive applications in different domains because of its simplicity, scalability and fault-tolerance. However, its uses in biomedical association mining are still very limited. In this paper, we investigate using MapReduce to efficiently mine the associations between biomedical terms extracted from a set of biomedical articles. First, biomedical terms were obtained by matching text to Unified Medical Language System (UMLS) Metathesaurus, a biomedical vocabulary and standard database. Then we developed a MapReduce algorithm that could be used to calculate a category of interestingness measures defined on the basis of a 2x2 contingency table. This algorithm consists of two MapReduce jobs and takes a stripes approach to reduce the number of intermediate results. Experiments were conducted using Amazon Elastic MapReduce (EMR) with an input of 3610 articles retrieved from two biomedical journals. Test results indicate that our algorithm has linear scalability.
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paper_title: MedicoPort: A medical search engine for all

paper_content:

We present a new next generation domain search engine called MedicoPort. MedicoPort is a medical search engine designed for the users with no medical expertise. It is enhanced with the domain knowledge obtained from Unified Medical Language System (UMLS) to increase the effectiveness of the searches. The power of the system is based on the ability to understand the semantics of web pages and the user queries. MedicoPort transforms a keyword search into a conceptual search. Through our system we present a topical web crawling technique and indexing techniques empowered by the semantics information. MedicoPort aims to generate maximum output with semantic value using minimum input from the user. Since MedicoPort is designed to help people seeking information about health on the web, our target users are not medical specialists who can effectively use the special jargon of medicine and access medical databases. Medical experts have the advantage of shrinking the answer set by expressing several terms using medical terminology. MedicoPort provides the same advantage to its users through the automated use of the medical domain knowledge in the background. The results of our experiments indicate that, expanding the queries with domain knowledge, such as using the synonyms and partially or contextually relevant terms from UMLS, increase dramatically the relevance of an answer set produced by MedicoPort and the number of retrieved web pages that are relevant to the user request.
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paper_title: Event extraction from heterogeneous news sources

paper_content:

With the proliferation of news articles from thousands of different sources now available on the Web, summarization of such information is becoming increasingly important. Our research focuses on merging descriptions of news events from multiple sources, to provide a concise description that combines the information from each source. Specifically, we describe and evaluate methods for grouping sentences in news articles that refer to the same event. The key idea is to cluster the sentences, using two novel distance metrics. The first distance metric exploits regularities in the sequential structure of events within a document. The second metric uses a TFIDF-like weighting scheme, enhanced to capture word frequencies within events even though the events themselves are not known a priori. Typical news articles contain sentences that do not describe specific events. We use machine learning methods to differentiate between sentences that describe one or more events, and those that do not. We then remove non-event sentences before initiating the clustering process. We demonstrate that this approach achieves significant improvements in overall clustering performance.
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paper_title: Mining Information Extraction Models for HmtDB annotation

paper_content:

Advances of genome sequencing techniques have risen an overwhelming increase in the literature on discovered genes, proteins and their role in biological processes. However, the biomedical literature remains a greatly unexploited source of biological information. Information extraction (IE) techniques are necessary to map this information into structured representations that allow facts relating domain-relevant entities to be automatically recognized. In this paper, we present a framework that supports biologists in the task of automatic extraction of information from texts. The framework integrates a data mining module that discovers extraction rules from a set of manually labelled texts. Extraction models are subsequently applied in an automatic mode on unseen texts. We report an application to a real-world dataset composed by publications selected to support biologists in the annotation of the HmtDB database
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paper_title: Towards Extracting Supporting Information About Predicted Protein-Protein Interactions

paper_content:

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.
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paper_title: Intelligent information mining from veterinary clinical records and open source repository

paper_content:

This paper reports an implementation of an intelligent mining approach from veterinary clinical records and an external source of information. The system retrieves information from a local veterinary clinical database and then complements this information with related records from an external source, OAIster. It utilizes text-mining, web service technologies and domain knowledge, in order to extract keywords, to retrieve related records from an external source, and to filter the extracted keywords list. This study meets a practical challenge encountered at the School of Veterinary and Biomedical Sciences at Murdoch University. The results indicate that the system can be used to increase the limited knowledge within a local source by complementing it with related records from an external source. Moreover, the system also reduces information overload by only retrieving a set of related information from an external source. Finally, domain knowledge can be used to filter the extracted keywords, in this case, selected medical keywords from the extracted keyword list.
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paper_title: NEW FRONTIERS IN BIOMEDICAL TEXT MINING

paper_content:

To paraphrase Gildea and Jurafsky [7], the past few years have been exhilarating ones for biomedical language processing. In less than a decade, we have seen an amazing increase in activity in text mining in the genomic domain [20]. The first textbook on biomedical text mining with a strong genomics focus appeared in 2005 [3]. The following year saw the establishment of a national center for text mining under the leadership of committed members of the BioNLP world [2], and two shared tasks [10,9] have led to the creation of new datasets and a very large community. These years have included considerable progress in some areas. The TREC Genomics track has brought an unprecedented amount of attention to the domain of biomedical information retrieval [8] and related tasks such as document classification [5] and question-answering, and the BioCreative shared task did the same for genomic named entity recognition, entity normalization, and information extraction [10]. Recent meetings have pushed the focus of biomedical NLP into new areas. A session at the Pacific Symposium on Biocomputing (PSB) 2006 [6] focussed on systems that linked multiple biological data sources, and the BioNLP’06 meeting [20] focussed on deeper semantic relations. However, there remain many application areas and approaches in which there is still an enormous amount of work to be done. In an attempt to facilitate movement of the field in those directions, the Call for Papers for this year’s PSB natural language processing session was written to address some of the potential “New Frontiers” in biomedical text mining. We solicited work in these specific areas:
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paper_title: Knowledge based word-concept model estimation and refinement for biomedical text mining

paper_content:

Display Omitted We describe a method to generate word-concept statistical models from a knowledge baseThis method integrates knowledge base descriptions and corpora informationWord sense disambiguation with this method is better than state-of-the-art approachesRanking of citation with the model improves the performance of baseline approaches Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation.In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences.The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches.
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paper_title: Mining Gene-centric Relationships from Literature to Support Drug Discovery

paper_content:

Identifying drug target candidates is an important task for early development throughout the drug discovery process. This process is supported by the development of new high-throughput technologies that enable better understanding of disease mechanism. With the push for personalized medicine, more experimental data are produced to identify how the genetics differ among individuals with respect to disease mechanism and drug response. It becomes critical to facilitate effective analysis of the large amount of biological data. In this paper, we describe our solution in employing text mining as a technique for finding scientific information for target and biomarker discovery from the biomedical literature. Additionally, we discuss how the extracted knowledge can be an effective resource for the analysis of biological data such as next-generation sequencing data.
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paper_title: Text-mining and information-retrieval services for molecular biology

paper_content:

Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators.
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paper_title: Quantifying the Impact and Extent of Undocumented Biomedical Synonymy

paper_content:

Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendium of biomedical thesauri. First, we demonstrate that missing synonymy has a significant negative impact on named entity normalization, an important problem within the field of biomedical text mining. To estimate the amount synonymy currently missing from thesauri, we develop a probabilistic model for the construction of synonym terminologies that is capable of handling a wide range of potential biases, and we evaluate its performance using the broader domain of near-synonymy among general English words. Our model predicts that over 90% of these relationships are currently undocumented, a result that we support experimentally through "crowd-sourcing." Finally, we apply our model to biomedical terminologies and predict that they are missing the vast majority (>90%) of the synonymous relationships they intend to document. Overall, our results expose the dramatic incompleteness of current biomedical thesauri and suggest the need for "next-generation," high-coverage lexical terminologies.
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paper_title: A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora

paper_content:

A number of techniques such as information extraction, document classification, document clustering and information visualization have been developed to ease extraction and understanding of information embedded within text documents. However, knowledge that is embedded in natural language texts is difficult to extract using simple pattern matching techniques and most of these methods do not help users directly understand key concepts and their semantic relationships in document corpora, which are critical for capturing their conceptual structures. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured texts that computers can not interpret very easily. In this paper, we have presented a novel Biomedical Knowledge Extraction and Visualization framework, BioKEVis to identify key information components from biomedical text documents. The information components are centered on key concepts. BioKEVis applies linguistic analysis and Latent Semantic Analysis (LSA) to identify key concepts. The information component extraction principle is based on natural language processing techniques and semantic-based analysis. The system is also integrated with a biomedical named entity recognizer, ABNER, to tag genes, proteins and other entity names in the text. We have also presented a method for collating information extracted from multiple sources to generate semantic network. The network provides distinct user perspectives and allows navigation over documents with similar information components and is also used to provide a comprehensive view of the collection. The system stores the extracted information components in a structured repository which is integrated with a query-processing module to handle biomedical queries over text documents. We have also proposed a document ranking mechanism to present retrieved documents in order of their relevance to the user query.
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paper_title: A Survey of event extraction methods from text for decision support systems

paper_content:

Event extraction, a specialized stream of information extraction rooted back into the 1980s, has greatly gained in popularity due to the advent of big data and the developments in the related fields of text mining and natural language processing. However, up to this date, an overview of this particular field remains elusive. Therefore, we give a summarization of event extraction techniques for textual data, distinguishing between data-driven, knowledge-driven, and hybrid methods, and present a qualitative evaluation of these. Moreover, we discuss common decision support applications of event extraction from text corpora. Last, we elaborate on the evaluation of event extraction systems and identify current research issues. We identify data-driven, knowledge-driven, and hybrid event extraction approaches.A wide variety of decision support applications can benefit from event extraction.Pressing research issues to be addressed are scalability and domain dependencies.Evaluation with annotated data from standard benchmarks or crowdsourcing is advised.
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paper_title: Complex event extraction at PubMed scale

paper_content:

Motivation: There has recently been a notable shift in biomedical information extraction (IE) from relation models toward the more expressive event model, facilitated by the maturation of basic tools for biomedical text analysis and the availability of manually annotated resources. The event model allows detailed representation of complex natural language statements and can support a number of advanced text mining applications ranging from semantic search to pathway extraction. A recent collaborative evaluation demonstrated the potential of event extraction systems, yet there have so far been no studies of the generalization ability of the systems nor the feasibility of large-scale extraction. ::: ::: Results: This study considers event-based IE at PubMed scale. We introduce a system combining publicly available, state-of-the-art methods for domain parsing, named entity recognition and event extraction, and test the system on a representative 1% sample of all PubMed citations. We present the first evaluation of the generalization performance of event extraction systems to this scale and show that despite its computational complexity, event extraction from the entire PubMed is feasible. We further illustrate the value of the extraction approach through a number of analyses of the extracted information. ::: ::: Availability: The event detection system and extracted data are open source licensed and available at http://bionlp.utu.fi/. ::: ::: Contact: jari.bjorne@utu.fi
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paper_title: Extraction of drug-disease relations from MEDLINE abstracts

paper_content:

Biological research literature, as in many other domains of human activity, is a rich source of knowledge. MEDLINE is a huge database of biomedical information and life sciences; it provides information in the form of abstracts and documents. However, extracting this information leads to various problems, related to the types of information such as recognition of all terms related to the domain of texts, concepts associated with them, as well as identifying the types of relationships. In this context, we suggest in this paper an approach to extract disease-drug relations: in a first step, we employ Natural Language Processing techniques for the abstracts' preprocessing. In a second step we extract a set of features from the preprocessed abstracts. Finally we extract a disease-drug relation using machine learning classifier.
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paper_title: A Survey on Chemical Text Mining Techniques for Identifying Relationship Network between Drug Disease Genes and Molecules

paper_content:

Text mining plays essential roles in the field of Chemoinformatics to reveal unknown information. The enormous amount of biomedical information is available on internet and resides in the form of published articles, files, patents etc. As the rich source of data is growing massively, it is widely contributing to the scientific researchers. Text mining is the most widely used in field of Natural Language Processing. The Text Pre-processing and data analysis techniques applied on biomedical literature allows us to identify ad investigate new theories. Finding the association between the chemical entities like drug, disease, genes and molecules is the new area of focus for researchers. This paper presents the study on several approaches and techniques proposed for chemical text-mining to identify relationship network for drug-disease, disease-gene associations. In this paper, we focus on comparative analysis of various Text mining techniques used for chemical literature with their results evaluations as well as observations.
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paper_title: Collaborative semi-automatic annotation of the biomedical literature

paper_content:

The increasing availability of whole human genomes due to the improvements in high-throughput sequencing technologies makes the interpretation and annotation of data at a whole-genome scale more and more feasible. However, these tasks critically depend on the availability of knowledge already stored in databases or published in the scientific literature. This scenario requires new reliable and integrative information extraction systems to be available to the biomedical community. In this work we present a hybrid approach for mining the wealth of knowledge stored in the scientific literature. This approach is based on the use of efficient text-mining tools in combination with highly accurate collaborative human curation. BioNotate-2.0 is an open source, modular friendly tool which implements a collaborative semi-automatic annotation platform in which human and automated annotations are efficiently combined. BioNotate-2.0 also builds upon the Semantic Web, facilitating the dissemination of annotated facts into other resources and pipelines. BioNotate-2.0 allows any interested user to run his own annotation efforts or to contribute to an existing annotation project. To access and contribute to the BioNotate-2.0 annotation platform, please check: http://genome2.ugr.es/bionotate2/. BioNotate source code is available at: http://sourceforge.net/projects/bionotate/.
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paper_title: Text mining describes the use of statistical and epidemiological methods in published medical research

paper_content:

Abstract Objective To describe trends in the use of statistical and epidemiological methods in the medical literature over the past 2 decades. Study Design and Setting We obtained all 1,028,786 articles from the PubMed Central Open-Access archive (retrieved May 9, 2015). We focused on 113,450 medical research articles. A Delphi panel identified 177 statistical/epidemiological methods pertinent to clinical researchers. We used a text-mining approach to determine if a specific statistical/epidemiological method was encountered in a given article. We report the proportion of articles using a specific method for the entire cross-sectional sample and also stratified into three blocks of time (1995–2005; 2006–2010; 2011–2015). Results Numeric descriptive statistics were commonplace (96.4% articles). Other frequently encountered methods groups included statistical inferential concepts (52.9% articles), epidemiological measures of association (53.5% articles) methods for diagnostic/classification accuracy (40.1% articles), hypothesis testing (28.8% articles), ANOVA (23.2% articles), and regression (22.6% articles). We observed relative percent increases in the use of: regression (103.0%), missing data methods (217.9%), survival analysis (147.6%), and correlated data analysis (192.2%). Conclusions This study identified commonly encountered and emergent methods used to investigate medical research problems. Clinical researchers must be aware of the methodological landscape in their field, as statistical/epidemiological methods underpin research claims.
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Title: A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Section 1: INTRODUCTION
Description 1: Describe the current landscape of biomedical research, highlighting the exponential growth of unstructured biomedical data and the challenges associated with extracting useful information from this data.

Section 2: PURPOSE OF THE STUDY
Description 2: Discuss the rationale behind the study, including the challenges of information extraction in biomedical databases, and outline the major challenges and implications addressed by the research.

Section 3: Overview of Text Mining
Description 3: Provide a broad overview of text mining, including its definition, typical tasks, and importance in managing large-scale, unstructured textual data, especially in the biomedical field.

Section 4: Text Mining
Description 4: Explain the process of automated knowledge extraction from text, detailing the phases and interdisciplinary fields involved.

Section 5: Models and Methods Used in Text Mining
Description 5: Discuss the different models and methods previously used for text mining, focusing on information retrieval and integration methods.

Section 6: Biomedical Literature Mining
Description 6: Trace the history and evolution of text mining applications in biomedical literature, highlighting key tasks like document retrieval, information extraction, and the importance of summarization.

Section 7: Biomedical Text Mining Tasks
Description 7: Detail the specific tasks associated with biomedical text mining, such as document retrieval, prioritization, information extraction, knowledge discovery, and hypothesis generation.

Section 8: RELATED WORKS
Description 8: Summarize the previous studies conducted in biomedical text mining, covering various frameworks, methodologies, and applications discussed in the literature.

Section 9: Text Mining Methods
Description 9: Outline the different text mining methods proposed by previous researchers, with a focus on their advantages, limitations, and areas for improvement.

Section 10: Knowledge Extraction Methods
Description 10: Describe the various methods for knowledge extraction, including innovative frameworks and algorithms tailored for biomedical text mining.

Section 11: Biomedical’s Data Mapping Techniques
Description 11: Discuss techniques employed for mapping biomedical data, including hybrid approaches and the application of ontologies.

Section 12: MATERIALS AND METHODS
Description 12: Explain the research methodology, including the comprehensive literature search strategy and the criteria for article inclusion and exclusion.

Section 13: RESULTS AND DISCUSSION
Description 13: Present the findings of the review, discuss the strengths and limitations of various approaches, and provide potential directions for future research.

Section 14: CONCLUSION
Description 14: Summarize the study, highlighting major conclusions, future recommendations, and the broader implications for the field of biomedical text mining.

Section 15: Text Summarization
Description 15: Highlight the need for further research in text summarization, including subjective aspects, visualization methods, and impact assessments.

Section 16: Summarization Tool
Description 16: Discuss the importance of reference standards and corpora for advancing summarization tools across different applications.

Section 17: Databases
Description 17: Emphasize the growing interest in effective data retrieval and extraction, and the role of text mining in molecular biology and future knowledge discovery tools.

Section 18: Higher Performance
Description 18: Focus on improving classification and data mining techniques to achieve higher performance in biomedical text mining applications.