tailieunhanh - Báo cáo y học: "BoCaTFBS: a boosted cascade learner to refine the binding sites suggested by ChIP-chip experiments"

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: BoCaTFBS: a boosted cascade learner to refine the binding sites suggested by ChIP-chip experiments. | Method Open Access BoCaTFBS a boosted cascade learner to refine the binding sites suggested by ChIP-chip experiments Lu-yong Wang Michael Snyder and Mark Gerstein Addresses integrated Data Systems Department Siemens Corporate Research 755 College Road East Princeton New Jersey 08540 USA. Department of Molecular Cellular and Developmental Biology KBT 926 266 Whitney Ave Yale University New Haven Connecticut 06520 USA. Department of Molecular Biophysics and Biochemistry Bass 432A 266 Whitney Ave Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Bass 432A 266 Whitney Ave Yale University New Haven CT 06520 USA. Department of Computer Science 51 Prospect Street Yale University New Haven Connecticut 06520 USA. Correspondence Mark Gerstein. Email Published I November 2006 Genome Biology 2006 7 RI02 doi gb-2006-7- II-rI02 The electronic version of this article is the complete one and can be found online at http 2006 7 II RI02 Received 20 June 2006 Revised 29 August 2006 Accepted I November 2006 2006 Wang 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 Comprehensive mapping of transcription factor binding sites is essential in postgenomic biology. For this we propose a mining approach combining noisy data from ChIP chromatin immunoprecipitation -chip experiments with known binding site patterns. Our method BoCaTFBS uses boosted cascades of classifiers for optimum efficiency in which components are alternating decision trees it exploits interpositional correlations and it explicitly integrates massive negative information from ChIP-chip experiments. We applied BoCaTFBS within the ENCODE project and showed that it outperforms .

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