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Bioinformatics Advance Access published online on August 26, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn463
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Improving Subcellular Localization Prediction using Text Classification and the Gene Ontology

Alona Fyshe 1, Yifeng Liu 1, Duane Szafron 1,*, Russ Greiner 1 and Paul Lu 1

1Department of Computing Science, University of Alberta, Edmonton, AB, Canada, T6G 2E8

*To whom correspondence should be addressed. Prof. Duane Szafron, E-mail: duane{at}cs.ualberta.ca


  Abstract

Motivation: Each protein performs its functions within some specific locations in a cell. This subcellular location is important for understanding protein function and for facilitating its purification. There are now many computational techniques for predicting location based on sequence analysis and database information from homologs. A few recent techniques use text from biological abstracts: our goal is to improve the prediction accuracy of such text-based techniques. We identify three techniques for improving text-based prediction: a rule for ambiguous abstract removal, a mechanism for using synonyms from the Gene Ontology (GO) and a mechanism for using the GO hierarchy to generalize terms. We show that these three techniques can significantly improve the accuracy of protein subcellular-location predictors that use text extracted from PubMed abstracts whose references are recorded in Swiss-Prot.

Associate Editor: Prof. Martin Bishop

Received on July 23, 2008; revised on August 21, 2008; accepted on August 25, 2008

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