DDA-SKF: Predicting Drug-Disease Associations Using Similarity Kernel Fusion

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...

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Bibliographic Details
Published in:Frontiers in pharmacology 2022-01, Vol.12, p.784171
Main Authors: Gao, Chu-Qiao, Zhou, Yuan-Ke, Xin, Xiao-Hong, Min, Hui, Du, Pu-Feng
Format: Article
Language:English
Subjects:
drug repositioning
drug–disease association
Laplacian regularized least squares
orphan drugs
Pharmacology
similarity kernel fusion
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Summary: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).
ISSN:1663-9812
1663-9812
DOI:10.3389/fphar.2021.784171