Display Settings:

Format

Send to:

Choose Destination
PLoS One. 2013;8(2):e55656. doi: 10.1371/journal.pone.0055656. Epub 2013 Feb 8.

Mining skeletal phenotype descriptions from scientific literature.

Author information

  • 1School of ITEE, The University of Queensland, Australia. tudor.groza@uq.edu.au

Abstract

Phenotype descriptions are important for our understanding of genetics, as they enable the computation and analysis of a varied range of issues related to the genetic and developmental bases of correlated characters. The literature contains a wealth of such phenotype descriptions, usually reported as free-text entries, similar to typical clinical summaries. In this paper, we focus on creating and making available an annotated corpus of skeletal phenotype descriptions. In addition, we present and evaluate a hybrid Machine Learning approach for mining phenotype descriptions from free text. Our hybrid approach uses an ensemble of four classifiers and experiments with several aggregation techniques. The best scoring technique achieves an F-1 score of 71.52%, which is close to the state-of-the-art in other domains, where training data exists in abundance. Finally, we discuss the influence of the features chosen for the model on the overall performance of the method.

PMID:
23409017
[PubMed - indexed for MEDLINE]
PMCID:
PMC3568099
Free PMC Article

Images from this publication.See all images (1)Free text

Figure 1
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk