Work

Accelerated variant curation from scientific literature using biomedical text mining

Year: 2022

Type: article

Abstract: papers for WormBase. Our model has a precision of 82.59% for the gene-mutation matches tested on extracted text from 100 papers, and even recovers some data not discovered during manual curation. Code at: https://github.com/WormBase/genomic-info-from-papers.

Source: PubMed Central

DOI: 10.17912/micropub.biology.000578

Authors: Rishab Mallick, Valerio Arnaboldi, Paul Davis, Stavros Diamantakis, Magdalena Zarowiecki, Kevin Howe

Institutions: European Bioinformatics Institute, California Institute of Technology

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Biotechnology and Biological Sciences Research Council, National Human Genome Research Institute

Awards: G179289588, Wormbase ParaSite, WormBase ParaSite, G8957386422, WormBase: a core data resource for C. elegans and other nematodes