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Relation extraction software tools | Biomedication text mining

Relation extraction software tools | Biomedication text mining Rapidly evolving sequencing technologies have led to a dramatic rise in the number of published articles reporting associations between genomic variations and diseases. There is an estimate that over 10,000 articles are published each year mentioning such associations. Manually collecting this information is both expensive and time consuming. To assist this manual curation, several text-mining (TM) efforts have been attempted. However, most of these efforts are limited to identifying mutation mentions only. The majority utilize regular expressions to detect mutations, although there are some, like tmVar, that use conditional random fields (CRFs), and SETH, which implements an Extended Backus-Naur Form (EBNF) grammar. Only a few of these efforts extend the mutation detection method to associate the mutation with a disease phenotype. Most of these are search based TM tools that do not employ automatic extraction of the mutation-disease relationships expressed in articles.
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