Autoregressive modeling and feature analysis of DNA sequences
EURASIP Journal on Applied Signal Processing
Correction of misclassifications using a proximity-based estimation method
EURASIP Journal on Applied Signal Processing
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
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Symbolic signals are, in discrete-time, sequences of quantities that do not assume numeric values. In the most general case, these quantities have no mathematical structure other than that they are members of some set, but they can have a sequential structure. The authors show that processing such signals does not entail mapping them directly to the integers, which would impose more structure-ordering and arithmetic-than present in the data. The authors describe how linear estimation and prediction can be performed on symbolic sequences. They show how spectrograms can be computed from neural population responses and from DNA sequences.