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<p>One of the most important tasks in genomic analysis is the precise identification of protein-coding regions in Deoxyribo nucleic acid (DNA) sequences. The robust exon prediction methodology presented in this paper is based on Hadamard numerical mapping, Short-Time Discrete Fourier Transform (STDFT), Complete Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Packet Thresholding (WPT). In order to provide orthogonal and noise-resistant representation, the DNA sequence is first numerically encoded using Hadamard mapping. The frequency components, specifically the period-3 signal indicative of coding regions are subsequently extracted using STDFT. After the signal is broken down into Intrinsic Mode Functions (IMFs) using CEEMD, noise-dominant IMFs are removed using WPT and a self-correlation function. The resulting signal has higher exon peaks and less background noise. MATLAB simulations were conducted using two benchmark data sequences: F56F11.4 (C. elegans), PSMB5 (Mus musculus) and HMR195 dataset. Using the HMR195 dataset and gene sequences (C. elegans F56F11.4 and M. musculus PSMB5), the proposed approach demonstrated outstanding metrics like accuracy, sensitivity and specificity. The MATLAB simulated results show that the proposed method works well and is reliable for predicting exons.</p>

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A STDFT-CEEMD approach with wavelet packet thresholding for exon prediction in eukaryotic cells

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  • 发表日期：

    2026年4月3日

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  • Shaik Benarjee,
  • Naveen Kumar Vaegae

摘要

One of the most important tasks in genomic analysis is the precise identification of protein-coding regions in Deoxyribo nucleic acid (DNA) sequences. The robust exon prediction methodology presented in this paper is based on Hadamard numerical mapping, Short-Time Discrete Fourier Transform (STDFT), Complete Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Packet Thresholding (WPT). In order to provide orthogonal and noise-resistant representation, the DNA sequence is first numerically encoded using Hadamard mapping. The frequency components, specifically the period-3 signal indicative of coding regions are subsequently extracted using STDFT. After the signal is broken down into Intrinsic Mode Functions (IMFs) using CEEMD, noise-dominant IMFs are removed using WPT and a self-correlation function. The resulting signal has higher exon peaks and less background noise. MATLAB simulations were conducted using two benchmark data sequences: F56F11.4 (C. elegans), PSMB5 (Mus musculus) and HMR195 dataset. Using the HMR195 dataset and gene sequences (C. elegans F56F11.4 and M. musculus PSMB5), the proposed approach demonstrated outstanding metrics like accuracy, sensitivity and specificity. The MATLAB simulated results show that the proposed method works well and is reliable for predicting exons.