A binary matrix factorization algorithm for protein complex prediction

We propose a binary matrix factorization (BMF) algorithm under the Bayesian Ying-Yang (BYY) harmony learning, to detect protein complexes by clustering the proteins which share similar interactions through factorizing the binary adjacent matrix of the protein-protein interaction (PPI) network. The proposed BYY-BMF algorithm automatically determines the cluster number while this number is usually specified for most existing BMF algorithms. Also, BYY-BMF's clustering results does not depend on any parameters or thresholds, unlike the Markov Cluster Algorithm (MCL) that relies on a so-called inflation parameter. On synthetic PPI networks, the predictions evaluated by the known annotated complexes indicate that BYY-BMF is more robust than MCL for most cases. Moreover, BYY-BMF obtains a better balanced prediction accuracies than MCL and a spectral analysis method, on real PPI networks from the MIPS and DIP databases.