AUTHOR=Gao Chu-Qiao , Zhou Yuan-Ke , Xin Xiao-Hong , Min Hui , Du Pu-Feng TITLE=DDA-SKF: Predicting Drug–Disease Associations Using Similarity Kernel Fusion JOURNAL=Frontiers in Pharmacology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.784171 DOI=10.3389/fphar.2021.784171 ISSN=1663-9812 ABSTRACT=

Drug repositioning provides a promising and efficient strategy to discover potential associations between drugs and diseases. Many systematic computational drug-repositioning methods have been introduced, which are based on various similarities of drugs and diseases. In this work, we proposed a new computational model, DDA-SKF (drug–disease associations prediction using similarity kernels fusion), which can predict novel drug indications by utilizing similarity kernel fusion (SKF) and Laplacian regularized least squares (LapRLS) algorithms. DDA-SKF integrated multiple similarities of drugs and diseases. The prediction performances of DDA-SKF are better, or at least comparable, to all state-of-the-art methods. The DDA-SKF can work without sufficient similarity information between drug indications. This allows us to predict new purpose for orphan drugs. The source code and benchmarking datasets are deposited in a GitHub repository (https://github.com/GCQ2119216031/DDA-SKF).