ArticlePatterns (New York, N.Y.)2021
HeTDR: Drug repositioning based on heterogeneous networks and text mining.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- STDrug enables spatially informed personalized drug repurposing from spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Uncertainty-aware hybrid deep generative framework for robust and explainable drug discovery.BMC bioinformatics · 2026Article
- A fused deep learning approach to transform drug repositioning.Communications chemistry · 2025Article
- Reposition of lenalidomide as a radiation protector based on LINCS gene expression signatures and its preclinical validation.Scientific reports · 2025Article
- HGTDR: Advancing drug repurposing with heterogeneous graph transformers.Bioinformatics (Oxford, England) · 2024Article
- DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.International journal of molecular sciences · 2024Article
- A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.PLoS computational biology · 2023Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions.Frontiers in microbiology · 2022Article
- An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.Frontiers in pharmacology · 2022Article
- Article
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Authors and funding
8 authors.
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Abstract
Using existing knowledge to carry out drug-disease associations prediction is a vital method for drug repositioning. However, effectively fusing the biomedical text and biological network information is one of the great challenges for most current drug repositioning methods. In this study, we propose a drug repositioning method based on heterogeneous networks and text mining (HeTDR). This model can combine drug features from multiple drug-related networks, disease features from biomedical corpora with the known drug-disease associations network to predict the correlation scores between drug and disease. Experiments demonstrate that HeTDR has excellent performance that is superior to that of state-of-the-art models. We present the top 10 novel HeTDR-predicted approved drugs for five diseases and prove our model is capable of discovering potential candidate drugs for disease indications.
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