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. 2011 Dec 1;27(23):3306-12.
doi: 10.1093/bioinformatics/btr573. Epub 2011 Oct 13.

Extraction of data deposition statements from the literature: a method for automatically tracking research results

Aurélie Névéol  1 , W John Wilbur, Zhiyong Lu
Affiliations

Extraction of data deposition statements from the literature: a method for automatically tracking research results

Aurélie Névéol et al. Bioinformatics. .

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.zip

Contact: aurelie.neveol@nih.gov; john.wilbur@nih.gov; zhiyong.lu@nih.gov

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Overview of annotated datasets used in this work.
Fig. 2.
Fig. 2.
Precision/recall curves for SVM and NB models built using all features.
See this image and copyright information in PMC

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