ArticleBioinformatics (Oxford, England)2020
Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- A case-based explainable graph neural network framework for mechanistic drug repositioning.Bioinformatics (Oxford, England) · 2026Article
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- SynOmics: integrating multi-omics data through feature interaction networks.Briefings in bioinformatics · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 2025Article
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
- HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug-disease association prediction.BMC biology · 2025Article
- Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery.Scientific reports · 2025Article
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Deep learning of multimodal networks with topological regularization for drug repositioning.Journal of cheminformatics · 2024Article
- DRML-Ensemble: drug repurposing method based on feature construction of multi-layer ensemble.Journal of molecular modeling · 2024Article
- HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.Bioinformatics (Oxford, England) · 2024Article
- Article
- Mining multi-center heterogeneous medical data with distributed synthetic learning.Nature communications · 2023Article
- Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.Nature communications · 2023Article
- Article
- Drug Repositioning Based on the Enhanced Message Passing and Hypergraph Convolutional Networks.Biomolecules · 2022Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- An automated multi-modal graph-based pipeline for mouse genetic discovery.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMining drug-disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug-disease associations. Importantly, the complexity of molecular target interactions, such as protein-protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context.
resultsIn this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug-protein, disease-protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug-disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis. AVAILABILITY AND IMPLEMENTATION: Source code and preprocessed datasets are at: https://github.com/zcwang0702/BiFusion.
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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.