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=Volume 12 - 2021

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 Fusions (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).