Improved Singular Value Decompositionbased Exons Prediction Approach Using Forwardbackward Filtering Exons prediction is a significant task in genomic signal processing to identify the locations of proteincoding regions in deoxyribonucleic acid (DNA) sequences. Singular value decomposition (SVD) is one of the approaches previously proposed for this purpose. This paper proposes a modified SVDbased exons prediction approach in order to improve the exons prediction accuracy. The improvement is achieved by employing the forwardbackward filtering technique in the preprocessing stage to remove the nonlinear phase distortion caused by the antinotch infinite impulse response (IIR) filter. In order to test the exons prediction performance of the proposed approach against that of the traditional approach, MATLAB simulation is carried out on the ASP67 dataset. The receiver operating characteristics (ROC) and precisionrecall curves in addition to the F1 score are utilized as performance evaluation metrics. The obtained results confirmed the merit of the proposed approach through achieving an improvement of 16.78% in the exons prediction accuracy at 10% false positive rate (FPR), compared to the traditional approach. In addition, the F1 score is increased by 5.16%.

Low Complexity Frequency Monitoring Filter for Fast Exon Prediction Sequence Analysis Over the last few years, the application of Digital Signal Processing (DSP) techniques for genomic sequence analysis has received great interest. Indeed, among its applications in genomic analysis, it has been demonstrated that DSP can be used to detect protein coding regions (exons) among noncoding regions in a DNA sequence. The period3 behavior exhibited by exons is one of its features that has been exploited in several developed algorithms for exon prediction. Identification of this periodicity in genomic sequences can be done by using different methods such as the wellknown Fast Fourier Transform (FFT) and the Goertzel algorithm for complexity reduction in which the reduction of computational time is a great challenge in genomic analysis. Therefore, this paper presents a novel one frequency analysis by using half of the arithmetic complexity of the Goertzel algorithm for gene prediction. Compared to the Intelxc2xaexe2x80x99s FFT (MKL) optimized function, the Goertzelxe2x80x99s (IPP) and the dedicated Goertzel compiled function with ICC on Xeon CPU (24 cores), the proposed method conserves the same accuracy provided by the referenced methods which will manifest a speedup of 3000, 10 and 2 compared to MKL FFT, IPP Goertzel and the dedicated Goertzel with ICC, respectively.