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: Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies. | Open Access Method Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies Bjorn Nilsson Petra Hakansson Mikael Johansson Sven Nelander and Thoas Fioretos Addresses Department of Clinical Genetics Lund University Hospital SE-221 85 Lund Sweden. Department of Automatic Control Royal Institute of Technology SE-100 44 Stockholm Sweden. Computational Biology Center Memorial Sloan-Kettering Cancer Center New York NY 10021 USA. Correspondence Bjorn Nilsson. Email Published 8 May 2007 Genome Biology 2007 8 R74 doi 186 gb-2007-8-5-r74 The electronic version of this article is the complete one and can be found online at http 2007 8 5 R74 Received 6 January 2007 Revised 2 April 2007 Accepted 8 May 2007 2007 Nilsson 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 Ontological analysis facilitates the interpretation of microarray data. Here we describe new ontological analysis methods which unlike existing approaches are threshold-free and statistically powerful. We perform extensive evaluations and introduce a new concept detection spectra to characterize methods. We show that different ontological analysis methods exhibit distinct detection spectra and that it is critical to account for this diversity. Our results argue strongly against the continued use of existing methods and provide directions towards an enhanced approach. Background A fundamental challenge in genome-wide gene-expression studies is to translate complex microarray data into an understanding of the biological conditions being studied. A widely used approach to this problem is ontological analysis - or functional gene-category analysis - the aim of which is to
