MOTIVATION: Text-mining mutation information from the literature becomes a critical part of the bioinformatics approach for the analysis and interpretation of sequence variations in complex diseases in the post-genomic era. It has also been used for assisting the creation of disease-related mutation databases. Most of existing approaches are rule-based and focus on limited types of sequence variations, such as protein point mutations. Thus, extending their extraction scope requires significant manual efforts in examining new instances and developing corresponding rules. As such, new automatic approaches are greatly needed for extracting different kinds of mutations with high accuracy. RESULTS: Here, we report tmVar, a text-mining approach based on conditional random field (CRF) for extracting a wide range of sequence variants described at protein, DNA and RNA levels according to a standard nomenclature developed by the Human Genome Variation Society. By doing so, we cover several important types of mutations that were not considered in past studies. Using a novel CRF label model and feature set, our method achieves higher performance than a state-of-the-art method on both our corpus (91.4 versus 78.1% in F-measure) and their own gold standard (93.9 versus 89.4% in F-measure). These results suggest that tmVar is a high-performance method for mutation extraction from biomedical literature. AVAILABILITY: tmVar software and its corpus of 500 manually curated abstracts are available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/pub/tmVar
MOTIVATION: Text-mining mutation information from the literature becomes a critical part of the bioinformatics approach for the analysis and interpretation of sequence variations in complex diseases in the post-genomic era. It has also been used for assisting the creation of disease-related mutation databases. Most of existing approaches are rule-based and focus on limited types of sequence variations, such as protein point mutations. Thus, extending their extraction scope requires significant manual efforts in examining new instances and developing corresponding rules. As such, new automatic approaches are greatly needed for extracting different kinds of mutations with high accuracy. RESULTS: Here, we report tmVar, a text-mining approach based on conditional random field (CRF) for extracting a wide range of sequence variants described at protein, DNA and RNA levels according to a standard nomenclature developed by the Human Genome Variation Society. By doing so, we cover several important types of mutations that were not considered in past studies. Using a novel CRF label model and feature set, our method achieves higher performance than a state-of-the-art method on both our corpus (91.4 versus 78.1% in F-measure) and their own gold standard (93.9 versus 89.4% in F-measure). These results suggest that tmVar is a high-performance method for mutation extraction from biomedical literature. AVAILABILITY: tmVar software and its corpus of 500 manually curated abstracts are available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/pub/tmVar
Authors: Remko Kuipers; Tom van den Bergh; Henk-Jan Joosten; Ronald H Lekanne dit Deprez; Marcel Mam Mannens; Peter J Schaap Journal: Hum Mutat Date: 2010-09 Impact factor: 4.878
Authors: J Gregory Caporaso; William A Baumgartner; David A Randolph; K Bretonnel Cohen; Lawrence Hunter Journal: Bioinformatics Date: 2007-05-11 Impact factor: 6.937
Authors: Alexander A Morgan; Zhiyong Lu; Xinglong Wang; Aaron M Cohen; Juliane Fluck; Patrick Ruch; Anna Divoli; Katrin Fundel; Robert Leaman; Jörg Hakenberg; Chengjie Sun; Heng-hui Liu; Rafael Torres; Michael Krauthammer; William W Lau; Hongfang Liu; Chun-Nan Hsu; Martijn Schuemie; K Bretonnel Cohen; Lynette Hirschman Journal: Genome Biol Date: 2008-09-01 Impact factor: 13.583
Authors: Antonio Jimeno Yepes; Andrew MacKinlay; Natalie Gunn; Christine Schieber; Noel Faux; Matthew Downton; Benjamin Goudey; Richard L Martin Journal: AMIA Annu Symp Proc Date: 2018-12-05