ArticleBioinformatics (Oxford, England)2016
Large-scale extraction of gene interactions from full-text literature using DeepDive.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
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- Biomedical event argument detection method based on multi-feature fusion and question-answer paradigm.Heliyon · 2024Article
- Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.European journal of medical research · 2024Article
- RelCurator: a text mining-based curation system for extracting gene-phenotype relationships specific to neurodegenerative disorders.Genes & genomics · 2023Article
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- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.Scientific reports · 2021Article
- Mining Proteome Research Reports: A Bird's Eye View.Proteomes · 2021Article
- Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive.Journal of biomedical informatics · 2021Article
- Text mining for modeling of protein complexes enhanced by machine learning.Bioinformatics (Oxford, England) · 2021Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Analyzing a co-occurrence gene-interaction network to identify disease-gene association.BMC bioinformatics · 2019Article
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- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Article
- Constructing a database for the relations between CNV and human genetic diseases via systematic text mining.BMC bioinformatics · 2018Article
- Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- A global network of biomedical relationships derived from text.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationA complete repository of gene-gene interactions is key for understanding cellular processes, human disease and drug response. These gene-gene interactions include both protein-protein interactions and transcription factor interactions. The majority of known interactions are found in the biomedical literature. Interaction databases, such as BioGRID and ChEA, annotate these gene-gene interactions; however, curation becomes difficult as the literature grows exponentially. DeepDive is a trained system for extracting information from a variety of sources, including text. In this work, we used DeepDive to extract both protein-protein and transcription factor interactions from over 100,000 full-text PLOS articles.
methodsWe built an extractor for gene-gene interactions that identified candidate gene-gene relations within an input sentence. For each candidate relation, DeepDive computed a probability that the relation was a correct interaction. We evaluated this system against the Database of Interacting Proteins and against randomly curated extractions.
resultsOur system achieved 76% precision and 49% recall in extracting direct and indirect interactions involving gene symbols co-occurring in a sentence. For randomly curated extractions, the system achieved between 62% and 83% precision based on direct or indirect interactions, as well as sentence-level and document-level precision. Overall, our system extracted 3356 unique gene pairs using 724 features from over 100,000 full-text articles. AVAILABILITY AND IMPLEMENTATION: Application source code is publicly available at https://github.com/edoughty/deepdive_genegene_app CONTACT: russ.altman@stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.