Predicting gene function using similarity learning

Background: Computational methods that make use of heterogeneous biological datasets to predict gene function provide a cost-effective and rapid way for annotating genomes. A common framework shared by many such methods is to construct a combined functional association network from multiple networks representing different sources of data, and use this combined network as input to network-based or kernel-based learning algorithms. In these methods, a key factor contributing to the prediction accuracy is the network quality, which is the ability of the network to reflect the functional relatedness of gene pairs. To improve the network quality, a large effort has been spent on developing methods for network integration. These methods, however, produce networks, which then remain unchanged, and nearly no effort has been made to optimize the networks after their construction. Results: Here, we propose an alternative method to improve the network quality. The proposed method takes as input a combined network produced by an existing network integration algorithm, and reconstructs this network to better represent the co-functionality relationships between gene pairs. At the core of the method is a learning algorithm that can learn a measure of functional similarity between genes, which we then use to reconstruct the input network. In experiments with yeast and human, the proposed method produced improved networks and achieved more accurate results than two other leading gene function prediction approaches. Conclusions: The results show that it is possible to improve the accuracy of network-based gene function prediction methods by optimizing combined networks with appropriate similarity measures learned from data. The proposed learning procedure can handle noisy training data and scales well to large genomes.