tailieunhanh - Báo cáo y học: "The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. | Method Open Access The Inferelator an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo Richard Bonneau David J Reiss Paul Shannon Marc Facciotti Leroy Hood Nitin S Baliga and Vesteinn Thorsson Addresses New York University Biology Department Center for Comparative Functional Genomics New York NY 10003 USA. Courant Institute NYU Department of Computer Science New York NY 10003 USA. institute for Systems Biology Seattle WA 98103-8904 USA. Correspondence Richard Bonneau. Email bonneau@ Published 10 May 2006 Genome Biology 2006 7 R36 doi 186 gb-2006-7-5-r36 The electronic version of this article is the complete one and can be found online at http 2006 7 5 R36 Received 24 October 2005 Revised 13 February 2006 Accepted 30 March 2006 2006 Bonneau 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 present a method the Inferelator for deriving genome-wide transcriptional regulatory interactions and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-I. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium s global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified. Background Distilling regulatory networks from large genomic proteomic and expression data sets is one of the most important mathematical problems in biology today. The development of accurate models of global regulatory networks is