title: nala: text mining natural language mutation mentions.
abstract: BACKGROUND
  The extraction of sequence variants from the literature remains an important task. Existing methods primarily target standard (ST) mutation mentions (e.g. 'E6V'), leaving relevant mentions natural language (NL) largely untapped (e.g. 'glutamic acid was substituted by valine at residue 6').
  
  
  RESULTS
  We introduced three new corpora suggesting named-entity recognition (NER) to be more challenging than anticipated: 28-77% of all articles contained mentions only available in NL. Our new method nala captured NL and ST by combining conditional random fields with word embedding features learned unsupervised from the entire PubMed. In our hands, nala substantially outperformed the state-of-the-art. For instance, we compared all unique mentions in new discoveries correctly detected by any of three methods (SETH, tmVar, or nala ). Neither SETH nor tmVar discovered anything missed by nala , while nala uniquely tagged 33% mentions. For NL mentions the corresponding value shot up to 100% nala -only.
  
  
  METHODS
  Source code, API and corpora freely available at: http://tagtog.net/-corpora/IDP4+ .
  
  
  BACKGROUND
  nala@rostlab.org.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching, Germany.
authors.forename: Juan Miguel
authors.initials: JM
authors.lastname: Cejuela
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching, Germany.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Bojchevski
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.
authors.forename: Carsten
authors.initials: C
authors.lastname: Uhlig
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|Microsoft, WA, Bellevue, USA.
authors.forename: Rustem
authors.initials: R
authors.lastname: Bekmukhametov
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|Ludwig Maximilian University, 80538 Munich & Siemens AG, Corporate Technology, Munich, Germany.
authors.forename: Sanjeev
authors.initials: S
authors.lastname: Kumar Karn
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.
authors.forename: Shpend
authors.initials: S
authors.lastname: Mahmuti
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|BITS-Pilani K. K. Birla Goa Campus, Goa, India.
authors.forename: Ashish
authors.initials: A
authors.lastname: Baghudana
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|Concur (Germany) GmbH, Frankfurt am Main, Germany.
authors.forename: Ankit
authors.initials: A
authors.lastname: Dubey
authors.affiliation: Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.
authors.forename: Venkata P
authors.initials: VP
authors.lastname: Satagopam
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.|Institute of Advanced Study (TUM-IAS) & Institute for Food and Plant Sciences WZW - Weihenstephan & New York Consortium on Membrane Protein Structure (NYCOMPS) & Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D057225:Data Mining / Q000379:methods*; D006801:Humans; D009154:Mutation; D009323:Natural Language Processing; D039781:PubMed; D012984:Software; D000069558:Unsupervised Machine Learning