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Bioinformatics Advance Access published online on October 12, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti058
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received March 15, 2004
Revised September 19, 2004
Accepted September 24, 2004

Article

Understanding protein dispensability through machine-learning analysis of high-throughput data

Yu Chen 1 and Dong Xu 1*

1 UT-ORNL Graduate School of Genome Science and Technology, Oak Ridge, TN, USA; Digital Biology Laboratory, Computer Science Department, University of Missouri-Columbia, Columbia, MO, USA

* To whom correspondence should be addressed. E-mail: xudong{at}missouri.edu.


  Abstract

Motivation: Protein dispensability is fundamental to understanding of gene function and evolution. Recent advances in generating high-throughput data such as genomic sequence data, protein-protein interaction data, gene-expression data, and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome scale.

Results: In our studies, protein dispensability is represented as a fitness score that is measured by the growth rate of gene-deletion mutants. Through analyses of high-throughput data in yeast Saccharomyces cerevisia, we found that a protein's dispensability had significant correlations with its evolutionary rate and duplication rate, as well as its connectivity in protein-protein interaction network and gene-expression correlation network. Neural network and support vector machine were applied to predict protein dispensability through high-throughput data. Our studies shed some lights on global characteristics of protein dispensability and evolution.

Availability: The original datasets for protein dispensability analysis and prediction, together with related scripts, are available at http://digbio.missouri.edu/~ychen/ProDispen/.


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