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Database (Oxford). 2016 Aug 31;2016. pii: baw122. doi: 10.1093/database/baw122. Print 2016.

Automated detection of discourse segment and experimental types from the text of cancer pathway results sections.

Author information

1
Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Marina del Rey, CA 90292, USA gully@usc.edu.
2
Carnegie Mellon University, Language Technologies Institute, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
3
Elsevier Research Data Services, Jericho, VT 05465, USA.

Abstract

Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles' Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data's meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide accurate, automated methods for biocuration. We also suggest the need for finer-grained curation of experimental methods used when constructing molecular biology databases.

PMID:
27580922
PMCID:
PMC5006090
DOI:
10.1093/database/baw122
[Indexed for MEDLINE]
Free PMC Article

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