Author:
Gao Chu-Qiao,Zhou Yuan-Ke,Xin Xiao-Hong,Min Hui,Du Pu-Feng
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).
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Subject
Pharmacology (medical),Pharmacology
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献