Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods. | Open Access Software Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods Priit Adlern Raivo Kolden Meelis Kull Aleksandr Tkachenko Hedi Peterson Juri Reimand and Jaak Vilo Addresses Institute of Molecular and Cell Biology Riia 23 51010 Tartu Estonia. Institute of Computer Science University of Tartu Liivi 2314 50409 Tartu Estonia. Quretec ulikooli 6a 51003 Tartu Estonia. n These authors contributed equally to this work. Correspondence JaakVilo. Email vilo@ Published 4 December 2009 Genome Biology 2009 10 R139 doi gb-2009-10-12-r1 39 The electronic version of this article is the complete one and can be found online at http 2009 10 12 R139 Received 13 August 2009 Revised 25 October 2009 Accepted 4 December 2009 2009 Adler 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 We present a web resource MEM Multi-Experiment Matrix for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at http mem . Rationale During the last decade the gene expression microarrays have become a standard tool in studying a large variety of biological questions 1 . Beginning from the first experiments 2 microarrays have been used for pinpointing disease-specific genes and drug targets 3 4 uncovering signaling networks 5 describing cellular processes 6 .
