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Bioinformatics Advance Access originally published online on July 31, 2006
Bioinformatics 2006 22(20):2488-2492; doi:10.1093/bioinformatics/btl419
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Partial correlation coefficient between distance matrices as a new indicator of protein–protein interactions

Tetsuya Sato 1,*, Yoshihiro Yamanishi 1, Katsuhisa Horimoto 2, Minoru Kanehisa 1 and Hiroyuki Toh 3

1 Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan
2 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology 2-42 Aomi, Koto-ku, Tokyo, 135-0064, Japan
3 Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan

*To Whom correspondence should be addressed.

Motivation: The computational prediction of protein–protein interactions is currently a major issue in bioinformatics. Recently, a variety of co-evolution-based methods have been investigated toward this goal. In this study, we introduced a partial correlation coefficient as a new measure for the degree of co-evolution between proteins, and proposed its use to predict protein–protein interactions.

Results: The accuracy of the prediction by the proposed method was compared with those of the original mirror tree method and the projection method previously developed by our group. We found that the partial correlation coefficient effectively reduces the number of false positives, as compared with other methods, although the number of false negatives increased in the prediction by the partial correlation coefficient.

Availability: The R script for the prediction of protein–protein interactions reported in this manuscript is available at http://timpani.genome.ad.jp/~parco/

Contact: sato{at}kuicr.kyoto-u.ac.jp

Supplementary information: The information is also available at the same site as the R script.

Received on March 3, 2006; revised on July 27, 2006; accepted on July 27, 2006

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