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: HuMiTar: A sequence-based method for prediction of human microRNA targets. | Algorithms for Molecular Biology BioMed Central Research HuMiTar A sequence-based method for prediction of human microRNA targets Jishou Ruan1 Hanzhe Chen1 Lukasz Kurgan 2 Ke Chen2 Chunsheng Kang3 and Peiyu Pu3 Open Access Address 1Chern Institute for Mathematics College of Mathematics and LPMC Nankai University Tianjin PR China 2Department of Electrical and Computer Engineering University of Alberta Canada and 3Neuro-oncology laboratory General Hospital of the Tianjin Medical University Tianjin PR China Email Jishou Ruan - jsruan@ Hanzhe Chen - jsruan@ Lukasz Kurgan - lkurgan@ Ke Chen - kchen1@ Chunsheng Kang - kangchunsheng@ Peiyu Pu - pupeiyu@ Corresponding author Published 22 December 2008 Received 3 May 2008 Algorithms for Molecular Biology 2008 3 16 doi 1748-7188-3-16 Accepted 22 December 2008 This article is available from http content 3 1 16 2008 Ruan 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 MicroRNAs miRs are small noncoding RNAs that bind to complementary partially complementary sites in the 3 untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR s seed region and implementation of cross-species conservation of the target site and machine learning ML methods that explore patterns that contrast true and false .
