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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: Editorial Advanced Signal Processing Techniques for Bioinformatics | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 51090 Pages 1-2 DOI 10.1155 ASP 2006 51090 Editorial Advanced Signal Processing Techniques for Bioinformatics Xue-Wen Chen 1 Sun Kim 2 Vladimir Pavlovic 3 and David P. Casasent4 1 Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS 66045 USA 2 School of Informatics Indiana University Bloomington IN 47408 USA 3 Department of Computer Science Rutgers University Piscataway NJ 08854 USA 4 Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA 15213 USA Received 5 January 2006 Accepted 5 January 2006 Copyright 2006 Xue-Wen Chen 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. The success of bioinformatics in recent years has been driven in part by advanced signal processing techniques estimation theory classification pattern recognition information theory networks imaging image processing coding theory and speech recognition. For example Fourier analysis methods are used to elucidate the relationship between sequence structure and function wavelet analysis methods have been applied in sequence comparison and classification and various image processing methods have been developed to improve microarray image quality. The development of advanced high-throughput technologies such as genome sequencing and whole genome expression analysis creates new opportunities and poses new challenges for the signal processing community. Analysis of data for life-science problems provides an interesting application domain for standard signal processing methods such as time series detection and prediction casual modeling and structure inference. At the same time this increasingly important life-science domain draws the need for novel and computationally
