Visualization and comparison of DNA sequences by use of three-dimensional trajectories
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Genomic Sequence Analysis Using Gap Sequences and Pattern Filtering
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Autoregressive modeling and feature analysis of DNA sequences
EURASIP Journal on Applied Signal Processing
On wavelet-based adaptive approach for gene comparison
International Journal of Intelligent Systems Technologies and Applications
Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A hybrid technique for the periodicity characterization of genomic sequence data
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Detection of tandem repeats in DNA sequences based on parametric spectral estimation
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Fourier analysis of symbolic data: A brief review
Digital Signal Processing
Mapping equivalence for symbolic sequences: theory and applications
IEEE Transactions on Signal Processing
The minimum entropy mapping spectrum of a DNA sequence
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Robust sounds of activities of daily living classification in two-channel audio-based telemonitoring
International Journal of Telemedicine and Applications
A DNA structure-based bionic wavelet transform and its application to DNA sequence analysis
Applied Bionics and Biomechanics
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Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals are discrete-time sequences that at, any particular index, have a value that is a member of a finite set of symbols. Set membership defines the only mathematical structure that symbolic sequences satisfy. Consequently, symbolic signals cannot be directly processed with existing signal processing algorithms designed for signals having values that are elements of a field (numeric signals) or a group. Generalizing an approach due to Stoffer (see Biometrika, vol.85, p.201-213, 1998), we extend time-frequency and time-scale analysis techniques to symbolic signals and describe a general linear approach to developing processing algorithms for symbolic signals. We illustrate our techniques by considering spectral and wavelet analyses of DNA sequences