DDA-SKF: Predicting Drug–Disease Associations Using Similarity Kernel Fusion - Gao, Chu-Qiao; Zhou, Yuan-Ke; Xin, Xiao-Hong; Min, Hui; Du, Pu-Feng (Frontiers in Pharmacology, 2022)
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).