Inferring Disease Associated Phosphorylation Sites via Random Walk on Multi Layer Heterogeneous Network
As protein phosphorylation plays an important role in numerous cellular processes, many studies have been undertaken to analyze phosphorylation related activities for drug design and disease treatment. However, although progresses have been made in illustrating the relationship between phosphorylation and diseases, no existing method focuses on disease associated phosphorylation sites prediction. In this work, we proposed a multi layer heterogeneous network model that makes use of the kinase information to infer disease phosphorylation site relationship and implemented random walk on the heterogeneous network. Experimental results reveal that multi layer heterogeneous network model with kinase layer is superior in inferring disease phosphorylation site relationship when comparing with existing random walk model and common used classification methods.
IEEE/ACM Transactions on Computational Biology and Bioinformatics