Computational Approaches for Predicting Drug-Disease Associations: A
  Comprehensive Review
Chunyan Ao Zhichao Xiao Lixin Guan Liang Yu
In recent decades, traditional drug research and development have been facing
challenges such as high cost, long timelines, and high risks. To address these
issues, many computational approaches have been suggested for predicting the
relationship between drugs and diseases through drug repositioning, aiming to
reduce the cost, development cycle, and risks associated with developing new
drugs. Researchers have explored different computational methods to predict
drug-disease associations, including drug side effects-disease associations,
drug-target associations, and miRNAdisease associations. In this comprehensive
review, we focus on recent advances in predicting drug-disease association
methods for drug repositioning. We first categorize these methods into several
groups, including neural network-based algorithms, matrixbased algorithms,
recommendation algorithms, link-based reasoning algorithms, and text mining and
semantic reasoning. Then, we compare the prediction performance of existing
drug-disease association prediction algorithms. Lastly, we delve into the
present challenges and future prospects concerning drug-disease associations.