Large language models in bioinformatics: applications and perspectives
Jiajia Liu Mengyuan Yang Yankai Yu Haixia Xu Kang Li Xiaobo Zhou
Large language models (LLMs) are a class of artificial intelligence models
based on deep learning, which have great performance in various tasks,
especially in natural language processing (NLP). Large language models
typically consist of artificial neural networks with numerous parameters,
trained on large amounts of unlabeled input using self-supervised or
semi-supervised learning. However, their potential for solving bioinformatics
problems may even exceed their proficiency in modeling human language. In this
review, we will present a summary of the prominent large language models used
in natural language processing, such as BERT and GPT, and focus on exploring
the applications of large language models at different omics levels in
bioinformatics, mainly including applications of large language models in
genomics, transcriptomics, proteomics, drug discovery and single cell analysis.
Finally, this review summarizes the potential and prospects of large language
models in solving bioinformatic problems.