ArticleNature communications2023
Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- CAREPath: semantic context-aware reasoning paths with mechanism-augmented embeddings for drug repurposing.Briefings in bioinformatics · 2026Article
- IIC-DTI: A Contrastive Learning Enhanced Inter-Intra Molecular Fusing Framework for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Enhancing link prediction in biomedical knowledge graphs with BioPathNet.Nature biomedical engineering · 2026Article
- AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare.BMC medical informatics and decision making · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- DRQuantum: a drug repurposing method by quantum walks on a multi-layered heterogeneous network.Scientific reports · 2026Article
- Metapath2vec and attention-driven heterogeneous graph learning for prioritizing TCM-derived small molecules in gastric cancer.Chinese medicine · 2026Article
- Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision.Pharmacopsychiatry · 2026Review
- Generating explainable hypotheses for drug repurposing with graph neural networks.Scientific reports · 2026Article
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing.bioRxiv : the preprint server for biology · 2026Article
- pyBiodatafuse: extending interoperability of data using modular queries across biomedical resources.Bioinformatics (Oxford, England) · 2026Article
- TransBrain: a computational framework for translating brain-wide phenotypes between humans and mice.Nature methods · 2026Article
- Whole-brain phenotype mapping between humans and mice.Nature methods · 2026Article
- Knowledge-graph embeddings for osteoarthritis candidate prediction.NPJ digital medicine · 2026Article
- A random-walk-based learning framework to uncover novel gene candidates for Alzheimer's disease therapy.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational drug repurposing aims to identify new indications for existing drugs by utilizing high-throughput data, often in the form of biomedical knowledge graphs. However, learning on biomedical knowledge graphs can be challenging due to the dominance of genes and a small number of drug and disease entities, resulting in less effective representations. To overcome this challenge, we propose a "semantic multi-layer guilt-by-association" approach that leverages the principle of guilt-by-association - "similar genes share similar functions", at the drug-gene-disease level. Using this approach, our model DREAMwalk: Drug Repurposing through Exploring Associations using Multi-layer random walk uses our semantic information-guided random walk to generate drug and disease-populated node sequences, allowing for effective mapping of both drugs and diseases in a unified embedding space. Compared to state-of-the-art link prediction models, our approach improves drug-disease association prediction accuracy by up to 16.8%. Moreover, exploration of the embedding space reveals a well-aligned harmony between biological and semantic contexts. We demonstrate the effectiveness of our approach through repurposing case studies for breast carcinoma and Alzheimer's disease, highlighting the potential of multi-layer guilt-by-association perspective for drug repurposing on biomedical knowledge graphs.
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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.