ArticleDatabase : the journal of biological databases and curation2013
Annotating the biomedical literature for the human variome.
Article in Database : the journal of biological databases and curation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- 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
- Evaluation of literature searching tools for curation of mismatch repair gene variants in hereditary colon cancer.Advanced genetics (Hoboken, N.J.) · 2021Article
- An extensive review of tools for manual annotation of documents.Briefings in bioinformatics · 2021Review
- Challenges in the construction of knowledge bases for human microbiome-disease associations.Microbiome · 2019Review
- CollaboNet: collaboration of deep neural networks for biomedical named entity recognition.BMC bioinformatics · 2019Article
- Transfer learning for biomedical named entity recognition with neural networks.Bioinformatics (Oxford, England) · 2018Article
- DES-Mutation: System for Exploring Links of Mutations and Diseases.Scientific reports · 2018Article
- A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- BioCreative VI Precision Medicine Track system performance is constrained by entity recognition and variations in corpus characteristics.Database : the journal of biological databases and curation · 2018Article
- Deep learning with word embeddings improves biomedical named entity recognition.Bioinformatics (Oxford, England) · 2017Article
- nala: text mining natural language mutation mentions.Bioinformatics (Oxford, England) · 2017Article
- 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
- Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016.CEUR workshop proceedings · 2016Article
- Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts.BMC medical informatics and decision making · 2016Article
- Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).Database : the journal of biological databases and curation · 2016Article
- Mining clinical attributes of genomic variants through assisted literature curation in Egas.Database : the journal of biological databases and curation · 2016Article
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7 authors.
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Abstract
This article introduces the Variome Annotation Schema, a schema that aims to capture the core concepts and relations relevant to cataloguing and interpreting human genetic variation and its relationship to disease, as described in the published literature. The schema was inspired by the needs of the database curators of the International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have application to genetic variation information in a range of diseases. The schema has been applied to a small corpus of full text journal publications on the subject of inherited colorectal cancer. We show that the inter-annotator agreement on annotation of this corpus ranges from 0.78 to 0.95 F-score across different entity types when exact matching is measured, and improves to a minimum F-score of 0.87 when boundary matching is relaxed. Relations show more variability in agreement, but several are reliable, with the highest, cohort-has-size, reaching 0.90 F-score. We also explore the relevance of the schema to the InSiGHT database curation process. The schema and the corpus represent an important new resource for the development of text mining solutions that address relationships among patient cohorts, disease and genetic variation, and therefore, we also discuss the role text mining might play in the curation of information related to the human variome. The corpus is available at http://opennicta.com/home/health/variome.
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