ArticleDatabase : the journal of biological databases and curation2016
BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations.
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- AI-Driven Combination Therapy for Counteracting Dysregulated Genes in Lung Adenocarcinoma: Contribution-Aware Metaheuristic for Drug Repurposing.Pharmaceuticals (Basel, Switzerland) · 2026Article
- JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps.Database : the journal of biological databases and curation · 2024Article
- RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature.Database : the journal of biological databases and curation · 2024Article
- A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature.Interdisciplinary sciences, computational life sciences · 2024Article
- Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource.Briefings in bioinformatics · 2024Review
- Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer.Scientific data · 2024Article
- ResidueFinder: extracting individual residue mentions from protein literature.Journal of biomedical semantics · 2021Article
- Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.Briefings in bioinformatics · 2021Review
- PGxCorpus, a manually annotated corpus for pharmacogenomics.Scientific data · 2020Article
- PubTator central: automated concept annotation for biomedical full text articles.Nucleic acids research · 2019Article
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
- deepBioWSD: effective deep neural word sense disambiguation of biomedical text data.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed.Nature biotechnology · 2018Article
- Deep learning of mutation-gene-drug relations from the literature.BMC bioinformatics · 2018Article
- tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine.Bioinformatics (Oxford, England) · 2018Article
- SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature.Journal of biomedical semantics · 2017Article
- Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.PLoS computational biology · 2016Article
- HiPub: translating PubMed and PMC texts to networks for knowledge discovery.Bioinformatics (Oxford, England) · 2016Article
- Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts.BMC medical informatics and decision making · 2016Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comprehensive knowledge of genomic variants in a biological context is key for precision medicine. As next-generation sequencing technologies improve, the amount of literature containing genomic variant data, such as new functions or related phenotypes, rapidly increases. Because numerous articles are published every day, it is almost impossible to manually curate all the variant information from the literature. Many researchers focus on creating an improved automated biomedical natural language processing (BioNLP) method that extracts useful variants and their functional information from the literature. However, there is no gold-standard data set that contains texts annotated with variants and their related functions. To overcome these limitations, we introduce a Biomedical entity Relation ONcology COrpus (BRONCO) that contains more than 400 variants and their relations with genes, diseases, drugs and cell lines in the context of cancer and anti-tumor drug screening research. The variants and their relations were manually extracted from 108 full-text articles. BRONCO can be utilized to evaluate and train new methods used for extracting biomedical entity relations from full-text publications, and thus be a valuable resource to the biomedical text mining research community. Using BRONCO, we quantitatively and qualitatively evaluated the performance of three state-of-the-art BioNLP methods. We also identified their shortcomings, and suggested remedies for each method. We implemented post-processing modules for the three BioNLP methods, which improved their performance.Database URL:http://infos.korea.ac.kr/bronco.
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