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Báo cáo y học: "Classification methods for the development of genomic signatures from high-dimensional data"

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: Classification methods for the development of genomic signatures from high-dimensional data. | Open Access Method Classification methods for the development of genomic signatures from high-dimensional data Hojin Moon Hongshik Ahn Ralph L Kodell Chien-Ju Lin Songjoon Baek and James J Chen Addresses Division of Biometry and Risk Assessment National Center for Toxicological Research FDA NCTR Road Jefferson AR 72079 USA. Department of Applied Mathematics and Statistics Stony Brook University Stony Brook NY 11794-3600 USA. Correspondence Hojin Moon. Email hojin.moon@fda.hhs.gov Published 20 December 2006 Genome Biology 2006 7 R121 doi 10.1186 gb-2006-7- 12-r121 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2006 7 12 R121 Received 28 July 2006 Revised 23 October 2006 Accepted 20 December 2006 2006 Moon 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 Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions CERP for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical histopathological measurements to the genomic data. Background Providing guidance on specific therapies for pathologically distinct tumor types to maximize efficacy and minimize toxicity is important for cancer treatment 1 2 . For acute leukemia for instance different subtypes show very different responses to therapy reflecting the fact that they are molecularly distinct entities although they have very similar morphological .