Genome-Phenome Association Prediction by Deep Factorizing Heterogeneous Molecular Network

Genome-phenome association (GPA) play a crucial part in deciphering the complex pathology of phenotypes (i.e., traits and diseases). Heterogeneous network-based GPA solutions can model the complex connections between multi-types of molecules by viewing them as nodes, and give better results than using single/two-omics data alone, but they overwhelmingly need to project other molecules toward homogeneous gene/phenotype nodes for data fusion and prediction, such projections result in information loss. Matrix factorization based data fusion can avoid such projection by integrating multi-type data in a coherent way, but they typically perform linear factorization and cannot mine the nonlinear relationships between molecules, which compromise the GPA analysis. Furthermore, most of them can not synergy network topology and node attribution information in a principle way. In this paper, we propose a deep matrix factorization based solution (DeepGPA) to predict GPAs by fusing heterogeneous molecular network and diverse attributes of nodes. DeepGPA performs deep matrix factorization on the block adjacency matrices of heterogeneous network in a cooperative manner to obtain the nonlinear representations of different moleclues. In addition, it performs low-rank representation learning on the attribute data with the shared nonlinear representations. In this way, both the network topology and node attributes are jointly mined to explore the representations of molecules and complex interplays between molecules and phenotypes. DeepGPA then uses the representational vectors of gene and phenotype nodes to predict GPAs. Experimental results on Maize datasets confirm t hat Deep DPA out performs competitive methods by a large margin under different evaluation protocols.