ArticleBioinformatics (Oxford, England)2024
Drug repositioning with adaptive graph convolutional networks.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Spatial-spectral fusion enables drug repositioning by capturing indirect and long-range associations in biological networks.Bioinformatics (Oxford, England) · 2026Article
- GHF-ACL: A novel contrastive learning framework with multi-order graph structures for herb-disease association prediction.PLoS computational biology · 2026Article
- Extracellular cGAMP in health and disease.Molecular biomedicine · 2026Review
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- Predicting drug-target interactions and binding affinity using an optimized deep learning approach.Journal of computer-aided molecular design · 2026Article
- MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.Current issues in molecular biology · 2026Article
- Enhanced drug disease association prediction through multimodal data integration and meta path guided global local feature fusion.Scientific reports · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Benchmarking heterogeneous network-based methods for drug repurposing.NPJ systems biology and applications · 2025Article
- MVSGDR: multi-view stacked graph convolutional network for drug repositioning.Briefings in bioinformatics · 2025Article
- Dual-route embedding-aware graph neural networks for drug repositioning.Briefings in bioinformatics · 2025Article
- DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation.Nature communications · 2025Article
- Reposition of lenalidomide as a radiation protector based on LINCS gene expression signatures and its preclinical validation.Scientific reports · 2025Article
- CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction.BMC bioinformatics · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification.BMC genomics · 2024Article
- Heterogeneous graph contrastive learning with gradient balance for drug repositioning.Briefings in bioinformatics · 2024Article
- Some Aspects and Convergence of Human and Veterinary Drug Repositioning.Molecules (Basel, Switzerland) · 2024Review
- Predicting potential microbe-disease associations based on dual branch graph convolutional network.Journal of cellular and molecular medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationDrug repositioning is an effective strategy to identify new indications for existing drugs, providing the quickest possible transition from bench to bedside. With the rapid development of deep learning, graph convolutional networks (GCNs) have been widely adopted for drug repositioning tasks. However, prior GCNs based methods exist limitations in deeply integrating node features and topological structures, which may hinder the capability of GCNs.
resultsIn this study, we propose an adaptive GCNs approach, termed AdaDR, for drug repositioning by deeply integrating node features and topological structures. Distinct from conventional graph convolution networks, AdaDR models interactive information between them with adaptive graph convolution operation, which enhances the expression of model. Concretely, AdaDR simultaneously extracts embeddings from node features and topological structures and then uses the attention mechanism to learn adaptive importance weights of the embeddings. Experimental results show that AdaDR achieves better performance than multiple baselines for drug repositioning. Moreover, in the case study, exploratory analyses are offered for finding novel drug-disease associations. AVAILABILITY AND IMPLEMENTATION: The soure code of AdaDR is available at: https://github.com/xinliangSun/AdaDR.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.