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Bioinformatics Advance Access published online on October 31, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm511
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Graph Sharpening plus Graph Integration: A Synergy that Improves Protein Functional Classification

Hyunjung Shin a,*, Andreas Martin Lisewski b,* and Olivier Lichtarge b

a Department of Industrial & Information Systems Engineering, Ajou University, San 5, Wonchun-dong, Yeoungtong-gu, 443–749, Suwon, Korea. b Department of Molecular & Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas, 77030, USA

To whom correspondence should be addressed. Prof. Hyunjung Shin, E-mail: shin{at}ajou.ac.kr


  Abstract

Motivation: Predicting protein function is a central problem in bioinformatics, and many approaches use partially or fully automated methods based on various combination of sequence, structure, and other information on proteins or genes. Such information establishes relationships between proteins that can be modeled most naturally as edges in graphs. A priori, however, it is often unclear which edges from which graph may contribute most to accurate predictions. For that reason, one established strategy is to integrate all available sources, or graphs as in graph integration [Tsuda et al., 2005], in the hope that the positive signals will add to each other. However, in the problem of functional prediction, noise, i.e. the presence of inaccurate or false edges, can still be large enough that integration alone has little effect on prediction accuracy. In order to reduce noise levels and to improve integration efficiency, we present here a recent method in graph-based learning, graph sharpening [Shin et al., 2006], which provides a theoretically firm yet intuitive and practical approach for disconnecting undesirable edges from protein similarity graphs. This approach has several attractive features: it is quick, scalable in the number of proteins, robust with respect to errors, and tolerant of very diverse types of protein similarity measures.

Results:

Availability: http://odin.mdacc.tmc.edu/jhu/lysatearray-analysis/.

Associate Editor: Dr. Jonathan Wren

* Both authors contributed equally to this work.

Received on May 4, 2007; revised on August 24, 2007; accepted on October 8, 2007

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