ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2014
Exploring the pharmacogenomics knowledge base (PharmGKB) for repositioning breast cancer drugs by leveraging Web ontology language (OWL) and cheminformatics approaches.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Self-management interventions for chronic kidney disease: a systematic review and meta-analysis.BMC nephrology · 2019Pooled it
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- 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
- DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.International journal of molecular sciences · 2024Article
- Drug repositioning in thyroid cancer treatment: the intriguing case of anti-diabetic drugs.Frontiers in pharmacology · 2023Review
- Computationally repurposing drugs for breast cancer subtypes using a network-based approach.BMC bioinformatics · 2022Article
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- Review
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer-ATTM.BLADE.Journal of personalized medicine · 2021Article
- SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks.BMC bioinformatics · 2021Article
- 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
- Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data.Journal of medical Internet research · 2019Article
- Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.European journal of medicinal chemistry · 2019Review
- The assessment of efficient representation of drug features using deep learning for drug repositioning.BMC bioinformatics · 2019Article
- A review of computational drug repurposing.Translational and clinical pharmacology · 2019Review
- Rare disease knowledge enrichment through a data-driven approach.BMC medical informatics and decision making · 2019Article
- Use of big data in drug development for precision medicine: an update.Expert review of precision medicine and drug development · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Computational drug repositioning leverages computational technology and high volume of biomedical data to identify new indications for existing drugs. Since it does not require costly experiments that have a high risk of failure, it has attracted increasing interest from diverse fields such as biomedical, pharmaceutical, and informatics areas. In this study, we used pharmacogenomics data generated from pharmacogenomics studies, applied informatics and Semantic Web technologies to address the drug repositioning problem. Specifically, we explored PharmGKB to identify pharmacogenomics related associations as pharmacogenomics profiles for US Food and Drug Administration (FDA) approved breast cancer drugs. We then converted and represented these profiles in Semantic Web notations, which support automated semantic inference. We successfully evaluated the performance and efficacy of the breast cancer drug pharmacogenomics profiles by case studies. Our results demonstrate that combination of pharmacogenomics data and Semantic Web technology/Cheminformatics approaches yields better performance of new indication and possible adverse effects prediction for breast cancer drugs.
Indexed as
Identifiers
24297544PMC3909178What OpenQuestion holds
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.