Matrix analysis
Computing linear transforms of symbolic signals
IEEE Transactions on Signal Processing
Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
New aspects in numerical representations involved in DNA repeats detection
WSEAS Transactions on Signal Processing
New representations in DNA repeats detection
ISCGAV'08 Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision
Spectral representations of alpha satellite DNA
WSEAS Transactions on Information Science and Applications
Alpha satellite DNA analysis using spectrograms
MCBC'09 Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry
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
SoftCOM'09 Proceedings of the 17th international conference on Software, Telecommunications and Computer Networks
Identification of protein coding regions using antinotch filters
Digital Signal Processing
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We overview and discuss several methods for the Fourier analysis of symbolic data, such as DNA sequences, emphasizing their mutual connections. We consider the indicator sequence approach, the vector and the symbolic autocorrelation methods, and methods such as the spectral envelope, that for each frequency optimize the symbolic-no-numeric mapping to emphasize any periodic data features. We discuss the equivalence or connections between these methods. We show that it is possible to define the autocorrelation function of symbolic data, assuming only that we can compare any two symbols and decide if they are equal or distinct. The autocorrelation is a numeric sequence, and its Fourier transform can also be obtained by summing the squares of the Fourier transform of indicator sequences (zero/one sequences indicating the position of the symbols). Another interpretation of the spectrum is given, borrowing from the spectral envelope concept: among all symbolic-to-numeric mappings there is one that maximizes the spectral energy at each frequency, and leads to the spectrum.