Title
Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions.
Abstract
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.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.The R script for the prediction of protein-protein interactions reported in this manuscript is available at http://timpani.genome.ad.jp/~parco/
Year
DOI
Venue
2006
10.1093/bioinformatics/btl419
Bioinformatics
Keywords
Field
DocType
distance matrix,false negative,false positive,co-evolution-based method,computational prediction,new indicator,r script,protein interaction,original mirror tree method,partial correlation coefficient,projection method,sequence alignment,evolution,partial correlation,phylogeny,bioinformatics,prediction,protein protein interaction,coevolution
Correlation coefficient,Partial correlation,Protein–protein interaction,Matthews correlation coefficient,Distance matrices in phylogeny,Algorithm,Projection method,R language,Mathematics,False positive paradox
Journal
Volume
Issue
ISSN
22
20
1367-4811
Citations 
PageRank 
References 
5
0.55
8
Authors
5
Name
Order
Citations
PageRank
Tetsuya Sato151.22
Yoshihiro Yamanishi2126883.44
Katsuhisa Horimoto325936.77
Minoru Kanehisa44429707.80
Hiroyuki Toh519011.09