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<p>The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification accuracy of exons. So, an efficient computational model is needed. Therefore, for the first time, we are introducing an optimized convolutional neural network (optCNN) for classifying the exons and introns. The study aims to identify the best CNN model that provides improved accuracy for the classification of exons by utilizing the optimization algorithm. In this case, an African Vulture Optimization Algorithm (AVOA) is used for optimizing the layered architecture of the CNN model along with its hyperparameters. The CNN model generated with AVOA yielded a success rate of 97.95% for the GENSCAN training set and 95.39% for the HMR195 dataset. The proposed approach is compared with the state-of-the-art methods using AUC, F1-score, Recall, and Precision. The results reveal that the proposed model is reliable and denotes an inventive method due to the ability to automatically create the CNN model for the classification of exons and introns.</p>

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Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月30日

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  • K. Jayasree,
  • Malaya Kumar Hota

摘要

The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification accuracy of exons. So, an efficient computational model is needed. Therefore, for the first time, we are introducing an optimized convolutional neural network (optCNN) for classifying the exons and introns. The study aims to identify the best CNN model that provides improved accuracy for the classification of exons by utilizing the optimization algorithm. In this case, an African Vulture Optimization Algorithm (AVOA) is used for optimizing the layered architecture of the CNN model along with its hyperparameters. The CNN model generated with AVOA yielded a success rate of 97.95% for the GENSCAN training set and 95.39% for the HMR195 dataset. The proposed approach is compared with the state-of-the-art methods using AUC, F1-score, Recall, and Precision. The results reveal that the proposed model is reliable and denotes an inventive method due to the ability to automatically create the CNN model for the classification of exons and introns.