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Bioinformatics Advance Access published online on November 28, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm520
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

An efficient strategy for extensive integration of diverse biological data for protein function prediction

Hon Nian Chua 1,*, Wing-Kin Sung 2 and Limsoon Wong 2

1Graduate School for Integrated Sciences and Engineering, and 2School of Computing, National University of Singapore, Singapore

*To whom correspondence should be addressed. Mr. Hon Nian Chua, E-mail: g0306417{at}nus.edu.sg


  Abstract

Motivation: With the increasing availability of diverse biological information, protein function prediction approaches have converged towards integration of heterogeneous data. Many adapted existing techniques, such as machine learning and probabilistic methods, which have proven successful on specific data types. However, the impact of these approaches is hindered by a couple of factors. Firstly, there is little comparison between existing approaches. This is in part due to a divergence in the focus adopted by different works, which makes comparison difficult or even fuzzy. Secondly, there seems to be over-emphasis on the use of computationally demanding machine learning methods, which runs counter to the surge in biological data. Analogous to the success of BLAST for sequence homology search, we believe that the ability to tap escalating quantity, quality and diversity of biological data is crucial to the success of automated function prediction as a useful instrument for the advancement of proteomic research. We address these problems by: 1) providing useful comparison between some prominent methods; 2) proposing Integrated Weighted Averaging (IWA) – a scalable, efficient and flexible function prediction framework that integrates diverse information using simple weighting strategies and a local prediction method. The simplicity of the approach makes it possible to make predictions based on on-the-fly information fusion.

Results: In addition to its greater efficiency, IWA performs exceptionally well against existing approaches. In the presence of cross-genome information, which is overwhelming for existing approaches, IWA makes even better predictions. We also demonstrate the significance of appropriate weighting strategies in data integration.

Associate Editor: Dr. Chris Stoeckert

Received on May 1, 2007; revised on October 12, 2007; accepted on October 12, 2007

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