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DDA-SKF: Predicting Drug–Disease Associations Using Similarity Kernel Fusion
- Source :
- Frontiers in Pharmacology, Vol 12 (2022)
- Publication Year :
- 2022
- Publisher :
- Frontiers Media S.A., 2022.
-
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).
Details
- Language :
- English
- ISSN :
- 16639812
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Pharmacology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.74aa2f586ad44520a694d33aac616fd2
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fphar.2021.784171