ArticlePLoS computational biology2012
Assessing drug target association using semantic linked data.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection.International journal of molecular sciences · 2024Article
- Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus.Tuberculosis (Edinburgh, Scotland) · 2024Article
- Review
- A network representation approach for COVID-19 drug recommendation.Methods (San Diego, Calif.) · 2022Article
- Network controllability solutions for computational drug repurposing using genetic algorithms.Scientific reports · 2022Article
- Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination.BMC bioinformatics · 2022Article
- Computational Methods for Drug Repurposing.Advances in experimental medicine and biology · 2022Article
- Drug repositioning: A bibliometric analysis.Frontiers in pharmacology · 2022Review
- Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions.Patterns (New York, N.Y.) · 2021Article
- A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-World Environmental Health Observations: Cohort Study and Informatics Application.JMIR medical informatics · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- NEDD: a network embedding based method for predicting drug-disease associations.BMC bioinformatics · 2020Article
- A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions.Journal of cheminformatics · 2020Review
- Understanding Drug Repurposing From the Perspective of Biomedical Entities and Their Evolution: Bibliographic Research Using Aspirin.JMIR medical informatics · 2020Article
- Validation strategies for target prediction methods.Briefings in bioinformatics · 2020Review
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.European journal of medicinal chemistry · 2019Review
- Article
- Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.Briefings in bioinformatics · 2019Review
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.
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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.