tmVar 3.0: an improved variant concept recognition and normalization tool

Author:

Wei Chih-Hsuan1ORCID,Allot Alexis1ORCID,Riehle Kevin2,Milosavljevic Aleksandar2,Lu Zhiyong1ORCID

Affiliation:

1. National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH) , Bethesda, MD 20894, USA

2. Department of Molecular and Human Genetics, Baylor College of Medicine , Houston, TX 77030, USA

Abstract

Abstract Motivation Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large scale. Despite multiple attempts in the past, existing tools are still of limited recognition scope and precision. Result We propose tmVar 3.0: an improved variant recognition and normalization system. Compared to its predecessors, tmVar 3.0 recognizes a wider spectrum of variant-related entities (e.g. allele and copy number variants), and groups together different variant mentions belonging to the same genomic sequence position in an article for improved accuracy. Moreover, tmVar 3.0 provides advanced variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry. tmVar 3.0 exhibits state-of-the-art performance with over 90% in F-measure for variant recognition and normalization, when evaluated on three independent benchmarking datasets. tmVar 3.0 as well as annotations for the entire PubMed and PMC datasets are freely available for download. Availability and implementation https://github.com/ncbi/tmVar3

Funder

National Institutes of Health Intramural Research Program

National Library of Medicine

NIH

NHGRI

Clinical Genome Resource

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference13 articles.

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4. nala: text mining natural language mutation mentions;Cejuela;Bioinformatics,2017

5. DeepVar: an end-to-end deep learning approach for genomic variant recognition in biomedical literature;Cheng;Proc. AAAI Conf. Artif. Intell,2020

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