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Báo cáo y học: "The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data"

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: The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data. | Open Access Method The LeFE algorithm embracing the complexity of gene expression in the interpretation of microarray data Gabriel S Eichler Mark Reimers David Kane and John N Weinstein Addresses Genomics and Bioinformatics Groups Laboratory of Molecular Pharmacology Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda Maryland 20892 USA. Tioinformatics Program Boston University Cummington St Boston Massachusetts 02215 USA. Virginia Commonwealth University Biostatistics Department E Marshall St Richmond Virginia 23284 USA. SRA International Fair Lakes Court Fairfax Virginia 22033 USA. Correspondence John N Weinstein. Email weinstein@dtpax2.ncifcrf.gov Published 10 September 2007 Genome Biology 2007 8 RI87 doi I0.II86 gb-2007-8-9-rI 87 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2007 8 9 RI87 Received 15 February 2007 Revised 29 June 2007 Accepted I0 September 2007 2007 Eichler 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 Interpretation of microarray data remains a challenge and most methods fail to consider the complex nonlinear regulation of gene expression. To address that limitation we introduce Learner of Functional Enrichment LeFE a statistical machine learning algorithm based on Random Forest and demonstrate it on several diverse datasets smoker never smoker breast cancer classification and cancer drug sensitivity. We also compare it with previously published algorithms including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology. Background Data from microarrays and other high-throughput molecular profiling platforms .