On Network-based Kernel Methods for Protein-Protein Interactions with Applications in Protein Functions Prediction

Abstract

Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding (LLE) kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions (PPI). The author first construct kernels based on PPI networks, then apply support vector machine (SVM) techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source.