Protein-protein interaction (PPI) prediction is a central task in achieving a better understanding of cellular and\nintracellular processes. Because high-throughput experimental methods are both expensive and time-consuming,\nand are also known of suffering from the problems of incompleteness and noise, many computational methods have\nbeen developed, with varied degrees of success. However, the inference of PPI network from multiple heterogeneous\ndata sources remains a great challenge. In this work, we developed a novel method based on approximate Bayesian\ncomputation and modified differential evolution sampling (ABC-DEP) and regularized laplacian (RL) kernel. The\nmethod enables inference of PPI networks from topological properties and multiple heterogeneous features including\ngene expression and Pfam domain profiles, in forms of weighted kernels. The optimal weights are obtained by ABCDEP,\nand the kernel fusion built based on optimal weights serves as input to RL to infer missing or new edges in the\nPPI network. Detailed comparisons with control methods have been made, and the results show that the accuracy of\nPPI prediction measured by AUC is increased by up to 23 %, as compared to a baseline without using optimal weights.\nThe method can provide insights into the relations between PPIs and various feature kernels and demonstrates strong\ncapability of predicting faraway interactions that cannot be well detected by traditional RL method.
Loading....