A binary matrix factorization algorithm for protein complex prediction

Background: identifying biologically relevant protein complexes from a large protein-protein interaction (ppi) network,is essential to understand the organization of biological systems. however,high-throughput experimental techniques that can produce a large amount of ppis are known to yield non-negligible rates of false-positives and false-negatives,making the protein complexes difficult to be identified.results: 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 a ppi network. the proposed byy-bmf algorithm automatically determines the cluster number while this number is pre-given 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. on real ppi networks from the mips and dip databases,byy-bmf obtains a better balanced prediction accuracies than mcl and a spectral analysis method,while mcl has its own advantages,e.g.,with good separation values. © 2011 tu et al; licensee biomed central ltd.