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Cell line name recognition in support of the identification of synthetic lethality in cancer from text
Author(s) -
Suwisa Kaewphan,
Sofie Van Landeghem,
Tomoko Ohta,
Yves Van de Peer,
Filip Ginter,
Sampo Pyysalo
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv570
Subject(s) - computer science , identification (biology) , named entity recognition , identifier , artificial intelligence , task (project management) , natural language processing , set (abstract data type) , normalization (sociology) , test set , unique identifier , line (geometry) , information retrieval , biology , programming language , botany , geometry , mathematics , management , sociology , anthropology , economics
The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus.

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