ArticleBioinformatics (Oxford, England)2024
TarKG: a comprehensive biomedical knowledge graph for target discovery.
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 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Benchmarking the impact of data leakage on the performance of knowledge graph embedding models for biomedical link prediction.Bioinformatics (Oxford, England) · 2026Article
- A graph retrieval-augmented generation pipeline for systematic drug target discovery: validation and application to ocular neovascularization.Briefings in bioinformatics · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization.Computational and structural biotechnology journal · 2026Article
- Advances in Computational Drug Repurposing, Driver Genes, and Therapeutics in Lung Adenocarcinoma.Biomolecules · 2025Review
- Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics.Frontiers in neurology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
motivationTarget discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery.
resultsWe established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1 143 313 entities and 32 806 467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG. AVAILABILITY AND IMPLEMENTATION: TarKG is accessible at https://tarkg.ddtmlab.org.
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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.