Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Digital Signal Processing: A Computer-Based Approach
Digital Signal Processing: A Computer-Based Approach
Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Efficient Localization of Hot Spots in Proteins Using a Novel S-Transform Based Filtering Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE Transactions on Signal Processing
Improved exon prediction with transforms by de-noising period-3 measure
Digital Signal Processing
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Signal processing-based algorithms for identification of coding sequences (CDS) in eukaryotes are non-data driven and exploit the presence of three-base periodicity in these regions for their detection. Three-base periodicity is commonly detected using short time Fourier transform (STFT) that uses a window function of fixed length. As the length of the protein coding and noncoding regions varies widely, the identification accuracy of STFT-based algorithms is poor. In this paper, a novel signal processing-based algorithm is developed by enabling the window length adaptation in STFT of DNA sequences for improving the identification of three-base periodicity. The length of the window function has been made adaptive in coding regions to maximize the magnitude of period-3 measure, whereas in the noncoding regions, the window length is tailored to minimize this measure. Simulation results on bench mark data sets demonstrate the advantage of this algorithm when compared with other non-data-driven methods for CDS prediction.