Dohkan Shinsuke, Koike Asako, Takagi Toshihisa
Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, (CB01) 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8581, Japan.
In Silico Biol. 2006;6(6):515-29.
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.
预测给定生物体中所有可能蛋白质对之间的相互作用（构建蛋白质 - 蛋白质相互作用图谱）是生物信息学中的一个关键课题。以前大多数基于监督机器学习的方法使用的数据集包含大致相同数量的相互作用蛋白质对（阳性）和非相互作用蛋白质对（阴性）来训练分类器，据估计会产生大量假阳性。考虑到先前研究中使用的阴性不能充分代表所有需要考虑的阴性，我们开发了一种基于多个支持向量机（SVM）的方法，该方法在预测酵母蛋白质对和人类蛋白质对之间的相互作用时使用的阴性比阳性更多。我们表明，随着用于训练的阴性数量增加，单个支持向量机的性能会提高，并且，如果有多个CPU可用，使用多个支持向量机的方法不仅有助于提高分类器的性能，还能减少训练所需的时间。我们的方法也可用于评估高通量相互作用的可靠性。
