Using similarity learning to improve network-based gene function prediction

A common strategy for predicting gene function from heterogeneous data sources is to construct a combined functional association network and use this network to infer gene function. In such approaches, the prediction accuracy largely depends on the quality of the network, and network optimization steps can lead to more accurate results. Existing methods, however, construct combined networks, which are then fixed, and no further optimization steps are performed. We propose a method that improves functional association networks before using them to predict gene function. The method uses an online learning algorithm to learn a similarity measure between pairs of genes, then uses this measure to construct new networks. The learning algorithm can handle noisy training signals and is fast enough to be practical. We evaluated the proposed method in predicting gene functions in two species (yeast and human). We found that our method produced networks with improved prediction accuracy, and outperformed two other state-of-the-art gene function prediction methods. A Matlab implementation of the method is available upon request.