ArticleDatabase : the journal of biological databases and curation2014
Literature mining of genetic variants for curation: quantifying the importance of supplementary material.
Article in Database : the journal of biological databases and curation, 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.
- Article
- Automatic detection and extraction of key resources from tables in biomedical papers.BioData mining · 2025Article
- Unlocking the potential of PubMed Central supplementary data files.Bioinformatics advances · 2025Article
- Tracking genetic variants in the biomedical literature using LitVar 2.0.Nature genetics · 2023Article
- Variant curation and interpretation in hereditary cancer genes: An institutional experience in Latin America.Molecular genetics & genomic medicine · 2023Article
- Assessing the use of supplementary materials to improve genomic variant discovery.Database : the journal of biological databases and curation · 2023Article
- Variomes: a high recall search engine to support the curation of genomic variants.Bioinformatics (Oxford, England) · 2022Article
- MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.Scientific reports · 2020Article
- PubTator central: automated concept annotation for biomedical full text articles.Nucleic acids research · 2019Article
- The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research.Natural product reports · 2019Review
- Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine.Frontiers in pharmacology · 2019Article
- Biomedical text mining for research rigor and integrity: tasks, challenges, directions.Briefings in bioinformatics · 2018Article
- tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine.Bioinformatics (Oxford, England) · 2018Article
- Article
- nala: text mining natural language mutation mentions.Bioinformatics (Oxford, England) · 2017Article
- Literature evidence in open targets - a target validation platform.Journal of biomedical semantics · 2017Article
- Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.PLoS computational biology · 2016Article
- Microtask crowdsourcing for disease mention annotation in PubMed abstracts.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- Database citation in supplementary data linked to Europe PubMed Central full text biomedical articles.Journal of biomedical semantics · 2015Article
- Associating disease-related genetic variants in intergenic regions to the genes they impact.PeerJ · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major focus of modern biological research is the understanding of how genomic variation relates to disease. Although there are significant ongoing efforts to capture this understanding in curated resources, much of the information remains locked in unstructured sources, in particular, the scientific literature. Thus, there have been several text mining systems developed to target extraction of mutations and other genetic variation from the literature. We have performed the first study of the use of text mining for the recovery of genetic variants curated directly from the literature. We consider two curated databases, COSMIC (Catalogue Of Somatic Mutations In Cancer) and InSiGHT (International Society for Gastro-intestinal Hereditary Tumours), that contain explicit links to the source literature for each included mutation. Our analysis shows that the recall of the mutations catalogued in the databases using a text mining tool is very low, despite the well-established good performance of the tool and even when the full text of the associated article is available for processing. We demonstrate that this discrepancy can be explained by considering the supplementary material linked to the published articles, not previously considered by text mining tools. Although it is anecdotally known that supplementary material contains 'all of the information', and some researchers have speculated about the role of supplementary material (Schenck et al. Extraction of genetic mutations associated with cancer from public literature. J Health Med Inform 2012;S2:2.), our analysis substantiates the significant extent to which this material is critical. Our results highlight the need for literature mining tools to consider not only the narrative content of a publication but also the full set of material related to a publication.
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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.