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Báo cáo y học: "Boolean implication networks derived from large scale, whole genome microarray datasets"

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: IBoolean implication networks derived from large scale, whole genome microarray datasets. | Open Access Method Boolean implication networks derived from large scale whole genome microarray datasets Debashis Sahoo David L Dill Andrew J Gentles Robert Tibshirani and Sylvia K Plevritis Addresses Department of Electrical Engineering Stanford University Stanford CA 94305 USA. Department of Computer Science Stanford University Stanford CA 94305 USA. Department of Radiology Stanford University Stanford CA 94305 USA. Department of Health Research and Policy and Department of Statistics Stanford University Stanford CA 94305 USA. Correspondence David L Dill. Email dill@cs.stanford.edu Published 30 October 2008 Genome Biology 2008 9 RI57 doi I0.II86 gb-2008-9- I0-r 157 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2008 9 I0 RI57 Received 28 June 2008 Revised 6 September 2008 Accepted 30 October 2008 2008 Sahoo et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract We describe a method for extracting Boolean implications if-then relationships in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans mice and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences tissue differences development and differentiation. New relationships are discovered that are preserved across all three species. Background A large and exponentially growing volume of gene expression data from microarrays is now available publicly. Since the quantity of data from around the world dwarfs the output of any individual laboratory there are opportunities for mining these data that can yield insights that would not be .