ReviewFrontiers in pharmacology2021
DDA-SKF: Predicting Drug-Disease Associations Using Similarity Kernel Fusion.
Review in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- Multi-DDA: drug-disease association prediction using a hybrid graph convolutional network with multi-modal drug representations.Bioinformatics advances · 2026Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.Interdisciplinary sciences, computational life sciences · 2025Article
- A Novel Drug-Disease Association Prediction Method Based on Deep Non-Negative Matrix Factorization with Local Graph Feature.Interdisciplinary sciences, computational life sciences · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 2025Article
- Joint embedding-classifier learning for interpretable collaborative filtering.BMC bioinformatics · 2025Article
- Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing.Scientific reports · 2025Article
- AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction.Frontiers in pharmacology · 2025Article
- A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.Briefings in bioinformatics · 2024Article
- ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis.Frontiers in genetics · 2023Article
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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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).
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