ArticleBioinformatics (Oxford, England)2024
HGTDR: Advancing drug repurposing with heterogeneous graph transformers.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Embeddings of clinical codes enable knowledge-grounded AI in medicine.NPJ digital medicine · 2026Article
- DMAPLM: A multimodal pretrained framework for computational drug repositioning.PLoS computational biology · 2026Article
- ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.Scientific reports · 2026Article
- An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.BMC biology · 2025Article
- A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.Molecular diversity · 2025Article
- Accurate prediction of synergistic drug combination using a multi-source information fusion framework.BMC biology · 2025Article
- Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms.BMC biology · 2025Article
- DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks.BMC bioinformatics · 2025Article
- SIGRN: Inferring Gene Regulatory Network with Soft Introspective Variational Autoencoders.International journal of molecular sciences · 2024Article
- Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.Briefings in bioinformatics · 2024Article
- PHPGAT: predicting phage hosts based on multimodal heterogeneous knowledge graph with graph attention network.Briefings in bioinformatics · 2024Article
- Deep active learning for multi label text classification.Scientific reports · 2024Article
- Multi-layer graph attention neural networks for accurate drug-target interaction mapping.Scientific reports · 2024Article
- Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network.Briefings in bioinformatics · 2024Article
- IPFMC: an iterative pathway fusion approach for enhanced multi-omics clustering in cancer research.Briefings in bioinformatics · 2024Article
- GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features.Scientific reports · 2024Article
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5 authors.
Funding
Abstract
motivationDrug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages.
resultsWe propose a new solution, Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing: (1) constructing a heterogeneous knowledge graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top 10 drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/bcb-sut/HGTDR and http://git.dml.ir/BCB/HGTDR.
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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.