Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: A new computational approach to analyze human protein complexes and predict novel protein interactions. | Open Access Method A new computational approach to analyze human protein complexes and predict novel protein interactions Sara Zanivan Ilaria Cascone Chiara Peyron Ivan Molineris Serena Marchio Michele CaselleH and Federico BussolinoH Addresses Department of Oncological Sciences and Division of Molecular Angiogenesis Institute for Cancer Research and T reatment IRCC University of Torino Medical School Strada Provinciale I-10060 Candiolo Turin Italy. tMax-Planck Institute for Biochemistry Department of Proteomics and Signal Transduction Am. Klopferspitz D-82152 Martinsried Germany. Inserm U528 Institut Curie 75248 Paris France. Department of Theoretical Physics University of Torino and INFN Via P Giuria 1 I-10125 Turin Italy. H These authors contributed equally to this work. Correspondence SaraZanivan. Email zanivan@ Published 4 December 2007 Genome Biology 2007 8 R256 doi gb-2007-8- l2-r256 The electronic version of this article is the complete one and can be found online at http 2007 8 12 R256 Received 24 August 2007 Revised 14 November 2007 Accepted 4 December 2007 2007 Zanivan et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1 we validated a-tubulin and early .
