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

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

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 interaction...

Prediction of Protein-Protein Interactions Based on Real-Valued Phylogenetic Profiles Using Partial Correlation Coefficient

The improvement of computational prediction method for protein-protein interactions (PPI) through genome comparisons is an important issue in bioinformatics. The phylogenetic profile method [2] has previously been developed for predicting protein functions and discovering specific PPI. This method basically stems from an assumption that interacting protein pairs are likely to have similar patte...

Prediction of Protein-Protein Interactions from Phylogenetic Trees Using Partial Correlation Coefficient

Inferring protein interactions from phylogenetic distance matrices

Finding the interacting pairs of proteins between two different protein families whose members are known to interact is an important problem in molecular biology. We developed and tested an algorithm that finds optimal matches between two families of proteins by comparing their distance matrices. A distance matrix provides a measure of the sequence similarity of proteins within a family. Since ...

The Rank Correlation Coefficient as an Indicator of the Product-Moment Correlation Coefficient for Small Samples

On the basis of 2200 small (10-100) samples obtained from four random number populations, it is shown that the rank correlation coefficient, p, is 88 adequate a measure of concordance 88 the Pearson product-moment correlation, T. The samples were obtained by using pseudo-random linear and normal number generators for bivariate populations with expected correlation cC'efficientB, r~, of 0.0 and ...