Improving the performance of an SVM-based method for predicting protein-protein interactions.

Predicting the interactions between all the possible pairs of proteins in a given organism (making a protein-protein interaction map) is a crucial subject in bioinformatics. Most of the previous methods based on supervised machine learning use datasets containing approximately the same number of interacting pairs of proteins (positives) and non-interacting pairs of proteins (negatives) for training a classifier and are estimated to yield a large number of false positives. Thinking that the negatives used in previous studies cannot adequately represent all the negatives that need to be taken into account, we have developed a method based on multiple Support Vector Machines (SVMs) that uses more negatives than positives for predicting interactions between pairs of yeast proteins and pairs of human proteins.