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- Title
Extraction of data deposition statements from the literature: a method for automatically tracking research results.
- Authors
Névéol, Aurélie; Wilbur, W. John; Lu, Zhiyong
- Abstract
Motivation: Research in the biomedical domain can have a major impact through open sharing of the data produced. For this reason, it is important to be able to identify instances of data production and deposition for potential re-use. Herein, we report on the automatic identification of data deposition statements in research articles.Results: We apply machine learning algorithms to sentences extracted from full-text articles in PubMed Central in order to automatically determine whether a given article contains a data deposition statement, and retrieve the specific statements. With an Support Vector Machine classifier using conditional random field determined deposition features, articles containing deposition statements are correctly identified with 81% F-measure. An error analysis shows that almost half of the articles classified as containing a deposition statement by our method but not by the gold standard do indeed contain a deposition statement. In addition, our system was used to process articles in PubMed Central, predicting that a total of 52 932 articles report data deposition, many of which are not currently included in the Secondary Source Identifier [si] field for MEDLINE citations.Availability: All annotated datasets described in this study are freely available from the NLM/NCBI website at http://www.ncbi.nlm.nih.gov/CBBresearch/Fellows/Neveol/DepositionDataSets.zipContact: aurelie.neveol@nih.gov; john.wilbur@nih.gov; zhiyong.lu@nih.govSupplementary Information: Supplementary data are available at Bioinformatics online.
- Subjects
UNITED States; DATA extraction; BIOINFORMATICS; SUPPORT vector machines; GENE expression; MEDLINE; NATIONAL Center for Biotechnology Information (U.S.)
- Publication
Bioinformatics, 2011, Vol 27, Issue 23, p3306
- ISSN
1367-4803
- Publication type
Article