ArticleF1000Research2014
Mutation extraction tools can be combined for robust recognition of genetic variants in the literature.
Article in F1000Research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- 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
- Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology.PloS one · 2020Article
- DES-Mutation: System for Exploring Links of Mutations and Diseases.Scientific reports · 2018Article
- Scaling up data curation using deep learning: An application to literature triage in genomic variation resources.PLoS computational biology · 2018Article
- Exploiting and assessing multi-source data for supervised biomedical named entity recognition.Bioinformatics (Oxford, England) · 2018Article
- tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine.Bioinformatics (Oxford, England) · 2018Article
- The SNPcurator: literature mining of enriched SNP-disease associations.Database : the journal of biological databases and curation · 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
- A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- nala: text mining natural language mutation mentions.Bioinformatics (Oxford, England) · 2017Article
- Mining the literature for genes associated with placenta-mediated maternal diseases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.PLoS computational biology · 2016Article
- Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts.BMC medical informatics and decision making · 2016Article
- Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts.BMC bioinformatics · 2016Article
- Article
- BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations.Database : the journal of biological databases and curation · 2016Article
- GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains.BioMed research international · 2015Article
- A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
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Authors and funding
2 authors.
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Abstract
As the cost of genomic sequencing continues to fall, the amount of data being collected and studied for the purpose of understanding the genetic basis of disease is increasing dramatically. Much of the source information relevant to such efforts is available only from unstructured sources such as the scientific literature, and significant resources are expended in manually curating and structuring the information in the literature. As such, there have been a number of systems developed to target automatic extraction of mutations and other genetic variation from the literature using text mining tools. We have performed a broad survey of the existing publicly available tools for extraction of genetic variants from the scientific literature. We consider not just one tool but a number of different tools, individually and in combination, and apply the tools in two scenarios. First, they are compared in an intrinsic evaluation context, where the tools are tested for their ability to identify specific mentions of genetic variants in a corpus of manually annotated papers, the Variome corpus. Second, they are compared in an extrinsic evaluation context based on our previous study of text mining support for curation of the COSMIC and InSiGHT databases. Our results demonstrate that no single tool covers the full range of genetic variants mentioned in the literature. Rather, several tools have complementary coverage and can be used together effectively. In the intrinsic evaluation on the Variome corpus, the combined performance is above 0.95 in F-measure, while in the extrinsic evaluation the combined recall performance is above 0.71 for COSMIC and above 0.62 for InSiGHT, a substantial improvement over the performance of any individual tool. Based on the analysis of these results, we suggest several directions for the improvement of text mining tools for genetic variant extraction from the literature.
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Registered trials
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