Learning Protein Functions from Bi-relational Graph of Proteins and Function Annotations

We propose here a multi-label semi-supervised learning algorithm, PfunBG, to predict protein functions, employing a bi-relational graph (BG) of proteins and function annotations. Different from most, if not all, existing methods that only consider the partially labeled proteinprotein interaction (PPI) network, the BG comprises three components, a PPI network, a function class graph induced from function annotations of such proteins, and a bipartite graph induced from function assignments. By referring to proteins and function classes equally as vertices, we exploit network propagation to measure how closely a specific function class is related to a protein of interest. The experiments on a yeast PPI network illustrate its effectiveness and efficiency.