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Editorial Advanced Signal Processing Techniques for Bioinformatics Xue-Wen Chen,1 Sun Kim,2 Vladimir Pavlovi´c,3 and David P. Casasent4 1 Department

of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA 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 2 School

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 efficient analysis approaches. The goal of this special issue is to present the applications of cutting-edge signal processing methods to bioinformatics. Eleven papers accepted for this special issue cover a broad range of topics, from RNA sequence analysis and gene expression analysis to protein structure predictions. The authors developed a variety of signal processing algorithms, such as artificial neural networks, decision trees, biclustering, matrix factorization, and FPGA reconfiguration methods, to tackle these central bioinformatics problems. The issue starts with two papers on gene sequence analyses. Churkin and Barash developed a pattern recognitionbased utility to perform mutational analysis and detect vulnerable spots within an RNA sequence that affect structures;

Babu et al. presented image processing/computer vision methods for automatic recovery and visualization of the 3D chromosome structure from a sequence of 2D tomographic reconstruction images taken through the nucleus of a cell. The advent of microarray techniques that allow for measuring the expression levels of tens of thousands of genes simultaneously has drawn increased interest