title: Weakly supervised learning of biomedical information extraction from curated data.
abstract: BACKGROUND
  Numerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exact mentions and their locations in the text required for supervised machine learning. This paper describes a general approach to information extraction using curated data as training examples. The idea is to formulate the problem as cost-sensitive learning from noisy labels, where the cost is estimated by a committee of weak classifiers that consider both curated data and the text.
  
  
  RESULTS
  We test the idea on two information extraction tasks of Genome-Wide Association Studies (GWAS). The first task is to extract target phenotypes (diseases or traits) of a study and the second is to extract ethnicity backgrounds of study subjects for different stages (initial or replication). Experimental results show that our approach can achieve 87% of Precision-at-2 (P@2) for disease/trait extraction, and 0.83 of F1-Score for stage-ethnicity extraction, both outperforming their cost-insensitive baseline counterparts.
  
  
  CONCLUSIONS
  The results show that curated biomedical databases can potentially be reused as training examples to train information extractors without expert annotation or refinement, opening an unprecedented opportunity of using "big data" in biomedical text mining.
authors.affiliation: Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. suj011@eng.ucsd.edu.
authors.forename: Suvir
authors.initials: S
authors.lastname: Jain
authors.affiliation: Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. ktumkur@eng.ucsd.edu.
authors.forename: Kashyap R
authors.initials: KR
authors.lastname: Tumkur
authors.affiliation: Department of Biomedical Informatics, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. tskuo@ucsd.edu.
authors.forename: Tsung-Ting
authors.initials: TT
authors.lastname: Kuo
authors.affiliation: Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. shbharga@eng.ucsd.edu.
authors.forename: Shitij
authors.initials: S
authors.lastname: Bhargava
authors.affiliation: Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. golin@eng.ucsd.edu.
authors.forename: Gordon
authors.initials: G
authors.lastname: Lin
authors.affiliation: Department of Biomedical Informatics, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. chunnan@ucsd.edu.
authors.forename: Chun-Nan
authors.initials: CN
authors.lastname: Hsu
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D066289:Data Curation; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D004194:Disease / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D018570:Risk Assessment
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