Affiliations: Department of Computational Biology, Graduate School
of Frontier Sciences, University of Tokyo, (CB01) 5-1-5 Kashiwanoha,
Kashiwa-shi, Chiba 277-8581, Japan | Central Research Laboratory, Hitachi Ltd., 1-280
Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
Abstract: 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. We
show that the performance of a single SVM improved as we increased the number
of negatives used for training and that, if more than one CPU is available, an
approach using multiple SVMs is useful not only for improving the performance
of classifiers but also for reducing the time required for training them. Our
approach can also be applied to assessing the reliability of high-throughput
interactions.
Keywords: Protein-protein interaction map, SVM, prediction, negative data
