Learning condition-dependent dynamical PPI networks from conflict-sensitive phosphorylation dynamics

An important issue in protein-protein interaction network studies is the identification of interaction dynamics. Two factors contribute to the dynamics. One, not all proteins may be expressed in a given cell, and two, competition may exist among multiple proteins for a particular protein domain. Taking into account these two factors, we propose a novel approach to predict protein-protein interaction network dynamics by learning from conflict-sensitive phosphorylation dynamics. We built a training model from conflict-sensitive phosphorylation dynamics. In this model, each node is not an individual protein but a protein-protein pair and is labeled with terms representing conditions in which the interaction should be observed. We mapped the protein pairs in a vector space, built hyper-edges over the interaction nodes, and developed rank-like SVM with Laplacian regularizers for PPI network dynamics prediction. We also employed the standard F1 measure for evaluating the effectiveness of classification results.