Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data. | Algorithms for Molecular Biology BioMed Central Research A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data Sahely Bhadra1 Chiranjib Bhattacharyya 1 2 Nagasuma R Chandra 2 and I Saira Mian3 Open Access Address Department of Computer Science and Automation Indian Institute of Science Bangalore Karnataka India 2Bioinformatics Centre Indian Institute of Science Bangalore Karnataka India and 3Life Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Email Sahely Bhadra - sahely@ Chiranjib Bhattacharyya - chiru@ Nagasuma R Chandra - nchandra@ I Saira Mian - smian@ Corresponding authors Published 24 February 2009 Received 30 May 2008 Algorithms for Molecular Biology 2009 4 5 doi l748-7l 88-4-5 Accepted 24 February 2009 This article is available from http content 4 1 5 2009 Bhadra 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 Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from highdimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of .
