Fundamentals of speech recognition
Fundamentals of speech recognition
Digital Signal Processing Using MATLAB
Digital Signal Processing Using MATLAB
Nucleic Acid and Protein Sequence Analysis
Nucleic Acid and Protein Sequence Analysis
Automated Chromosome Classification Using Wavelet-Based Band Pattern Descriptors
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Dynamic Programming
Pseudo-periodic partitions of biological sequences
Bioinformatics
Wavelet Theory and Its Application to Pattern Recognition
Wavelet Theory and Its Application to Pattern Recognition
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Linear predictive coding representation of correlated mutation for protein sequence alignment
Proceedings of the third international workshop on Data and text mining in bioinformatics
Possibilistic nonlinear dynamical analysis for pattern recognition
Pattern Recognition
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In bioinformatics and computational biology, methods for biological sequence comparison play the most important role for the interpretation of complex nucleotide and protein data such as the inference of relationships between genes, proteins and species; and the discovery of novel protein structures and functions. This type of inference is derived by sequence similarity matching on the databases of biological sequences. As many entire genomes have being determined at a rapid rate, computational methods for comparing genomic and protein sequences will be more essential for probing the complexity of genes, genomes, and molecular machines. In this paper we introduce a pattern-comparison algorithm, which is based on the mathematical concepts of linear predictive coding and its cepstral-distortion measures for the analyses of both DNA and protein sequences. The results obtained from several experiments on real datasets have shown the effectiveness of the proposed approach.