title: Cell line name recognition in support of the identification of synthetic lethality in cancer from text.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers.
  
  
  METHODS
  The manually annotated datasets, the cell line dictionary, derived corpora, NERsuite models and the results of the large-scale run on unannotated texts are available under open licenses at http://turkunlp.github.io/Cell-line-recognition/.
  
  
  BACKGROUND
  sukaew@utu.fi.
authors.affiliation: Turku Centre for Computer Science (TUCS), 20520 Turku, Finland, Department of Information Technology, University of Turku, 20014, Finland, University of Turku Graduate School (UTUGS), University of Turku, 20014, Finland.
authors.forename: Suwisa
authors.initials: S
authors.lastname: Kaewphan
authors.affiliation: Department of Plant Systems Biology, VIB, Ghent 9000, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium.
authors.forename: Sofie
authors.initials: S
authors.lastname: Van Landeghem
authors.affiliation: Textimi, Tokyo, Japan.
authors.forename: Tomoko
authors.initials: T
authors.lastname: Ohta
authors.affiliation: Department of Plant Systems Biology, VIB, Ghent 9000, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium, Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium, Genomics Research Institute, University of Pretoria, Pretoria, South Africa and.
authors.forename: Yves
authors.initials: Y
authors.lastname: Van de Peer
authors.affiliation: Department of Information Technology, University of Turku, 20014, Finland.
authors.forename: Filip
authors.initials: F
authors.lastname: Ginter
authors.affiliation: Department of Information Technology, University of Turku, 20014, Finland, Language Technology Lab (LTL), University of Cambridge, Cambridge CB3 9DA, United Kingdom.
authors.forename: Sampo
authors.initials: S
authors.lastname: Pyysalo
mesh_terms: D001185:Artificial Intelligence; D002460:Cell Line; D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005804:Genes, Lethal; D006801:Humans; D016247:Information Storage and Retrieval; D000069550:Machine Learning; D009369:Neoplasms / Q000235:genetics / Q000473:pathology*; D011642:Publications; D012660:Semantics; D009626:Terminology as Topic