ArticlePLoS computational biology2015
Learning the Structure of Biomedical Relationships from Unstructured Text.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed, 45 citations in OpenAlex.
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- Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations.Database : the journal of biological databases and curation · 2023Article
- Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.International journal of molecular sciences · 2023Review
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- Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.Briefings in bioinformatics · 2022Review
- Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease.PLoS computational biology · 2021Article
- Translational Knowledge Discovery Between Drug Interactions and Pharmacogenetics.Clinical pharmacology and therapeutics · 2020Review
- PGxCorpus, a manually annotated corpus for pharmacogenomics.Scientific data · 2020Article
- Pathway and network embedding methods for prioritizing psychiatric drugs.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Rapamycin - mTOR + BRAF = ? Using relational similarity to find therapeutically relevant drug-gene relationships in unstructured text.Journal of biomedical informatics · 2019Article
- Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke.Frontiers in neuroinformatics · 2019Article
- Essential Characteristics of Pharmacogenomics Study Publications.Clinical pharmacology and therapeutics · 2019Review
- A global network of biomedical relationships derived from text.Bioinformatics (Oxford, England) · 2018Article
- Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings.Database : the journal of biological databases and curation · 2018Article
- Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation.Database : the journal of biological databases and curation · 2018Article
- Mining the literature for genes associated with placenta-mediated maternal diseases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.Advances in experimental medicine and biology · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes). Unfortunately, this information is distributed throughout the unstructured text of over 23 million articles. The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions. Extracting these relationships from natural language sentences on such a large scale, however, requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas. Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice and sentence structure. We validate EBC's performance against manually-curated sets of (1) pharmacogenomic relationships from PharmGKB and (2) drug-target relationships from DrugBank, and use it to discover new drug-gene relationships for both knowledge bases. We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline, revealing unexpected structure that challenges current notions about how these relationships are expressed in text. For instance, we learn that newer experimental findings are described in consistently different ways than established knowledge, and that seemingly pure classes of relationships can exhibit interesting chimeric structure. The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining.
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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.