Selective Integration of Multiple Genomic Data for Biological Network Inference
In the field of computational biology, recently there has been a surge of interest in biological networks such as protein interaction networks, gene regulatory networks, or metabolic networks, which help us to understand the cellular machinery. Most of biological networks represent the relationships between genes or proteins. Namely the existence of edges means that the corresponding genes/proteins are related each other. In this paper, we are confronted with a problem to predict the interactions between genes/proteins. In our problem setting we are supposed to be given a part of the network and various genomic data such as gene expression data, localization data, and phylogenetic profile data. Our task is to predict the rest of the network. Yamanishi et al. [3] have already tackled the exactly same setting, and refer this problem to supervised network inference. Their approach uses canonical correlation analysis following the process of integration of multiple genomic data. The integration is done by linear combination of the multiple kernel matrices. We have developed another new method for this problem. Our method is based on information geometrical theory, and simultaneously infers the unknown subgraph and the weights of linear combination.