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: MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data. | Software Open Access MetaReg a platform for modeling analysis and visualization of biological systems using large-scale experimental data Igor Ulitsky Irit Gat-ViksH and Ron Shamir Addresses School of Computer Science Tel Aviv University Tel Aviv 69978 Israel. Computational Molecular Biology Department Max Planck Institute for Molecular Genetics Ihnestrasse 73 D-14195 Berlin Germany. H These authors contributed equally to this work. Correspondence Ron Shamir. Email rshamir@ Published 2 January 2008 Genome Biology 2008 9 R1 doi gb-2008-9-1-r1 The electronic version of this article is the complete one and can be found online at http 2008 9 1 R1 Received 4 July 2007 Revised 28 September 2007 Accepted 2 January 2008 2008 Ulitsky 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 MetaReg http metareg is a computational tool that models cellular networks and integrates experimental results with such models. MetaReg represents established knowledge about a biological system available today mostly in informal form in the literature as probabilistic network models with underlying combinatorial regulatory logic. MetaReg enables contrasting predictions with measurements model improvements and studying what-if scenarios. By summarizing prior knowledge and providing visual and computational aids it helps the expert explore and understand her system better. Rationale Given the recent accumulation of high throughput biological data the task of integrating and analyzing large-scale datasets is a major challenge. A variety of computational modeling approaches have been developed for the analysis of such datasets such as clustering 1 2 and topological .
