ReviewBiomolecules2022
Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.
Review in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- A Graph Neural Network Framework with Equivariance and Heterogeneity for Drug Repositioning.Interdisciplinary sciences, computational life sciences · 2026Article
- MPMB-DR: Meta-path Integration of Multi-source Biological Information for Drug Repositioning.Interdisciplinary sciences, computational life sciences · 2026Article
- Enhanced drug disease association prediction through multimodal data integration and meta path guided global local feature fusion.Scientific reports · 2026Article
- Multi-DDA: drug-disease association prediction using a hybrid graph convolutional network with multi-modal drug representations.Bioinformatics advances · 2026Article
- Benchmarking heterogeneous network-based methods for drug repurposing.NPJ systems biology and applications · 2025Article
- SMPR: a structure-enhanced multimodal drug‒disease prediction model for drug repositioning and cold start.Journal of cheminformatics · 2025Article
- FACT: Feature Aggregation and Convolution with Transformers for predicting drug classification code.Bioinformatics (Oxford, England) · 2025Article
- Overcoming temozolomide resistance in glioma: recent advances and mechanistic insights.Acta neuropathologica communications · 2025Review
- Improving drug repositioning accuracy using non-negative matrix tri-factorization.Scientific reports · 2025Article
- Improving drug repositioning with negative data labeling using large language models.Journal of cheminformatics · 2025Article
- HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer's disease.Journal of translational medicine · 2025Article
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins.Biomolecules · 2024Article
- Computational drug repositioning with attention walking.Scientific reports · 2024Article
- A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.Briefings in bioinformatics · 2024Article
- Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning.Biomedicines · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug-disease associations. This review reveals existing network-based approaches for predicting drug-disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug-drug similarities based on chemical structures and ATC codes, ontology-based disease-disease similarities, and drug-disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug-drug similarity measurement are crucial for improving disease-side prediction.
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Registered trials
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