Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007 Article ID 39382 7 pages doi 2007 39382 Research Article The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data J. Z. Song 1 K. M. Duan 2 T. Ware 3 and M. Surette2 1 Department of Animal and Avian Science 2413 Animal Science Center University of Maryland College Park MD 20742 USA 2 Department of Microbiology and Infectious Diseases and Department of Biochemistry and Molecular Biology Health Sciences Centre University of Calgary Calgary AB Canada T2N 4N1 3 Department of Mathematics University of Calgary Calgary AB Canada T2N 4N1 Received 4 December 2005 Revised 1 October 2006 Accepted 4 March 2007 Recommended by Ahmed H. Tewfik A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions. Copyright 2007 J. Z. Song et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION High-throughput gene expression techniques such as oligonucleotide and cDNA microarrays SAGE series analysis gene expression and promoter arrays 1-5 make it
