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

K Jayasree¹, Malaya Kumar Hota²

¹Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.

Scientific Reports

|January 30, 2025

Summary

This summary is machine-generated.

This study introduces an optimized convolutional neural network (optCNN) for genomic sequence analysis, improving exon and intron classification accuracy. The African Vulture Optimization Algorithm (AVOA) enhances the model, achieving high success rates.

Keywords:

African vulture optimization algorithm (AVOA)Convolutional neural network (CNN)Exons Modified Gabor wavelet transform (MGWT)Three base periodicity properties (TBP)

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Area of Science:

Genomic sequence analysis
Bioinformatics
Computational biology

Background:

Exon detection is crucial in genomic analysis, with existing signal-processing methods showing limitations in accuracy.
Improved computational models are needed to enhance the identification of exons and introns.

Purpose of the Study:

To introduce an optimized convolutional neural network (optCNN) for accurate exon and intron classification.
To identify the optimal CNN architecture and hyperparameters using an optimization algorithm for improved exon identification.

Main Methods:

An optimized convolutional neural network (optCNN) was developed for classifying exons and introns.
The African Vulture Optimization Algorithm (AVOA) was employed to optimize the CNN's layered architecture and hyperparameters.
Performance was evaluated using GENSCAN and HMR195 datasets.

Main Results:

The AVOA-optimized CNN achieved a 97.95% success rate on the GENSCAN training set and 95.39% on the HMR195 dataset.
Comparison with state-of-the-art methods using AUC, F1-score, Recall, and Precision demonstrated the model's reliability.
The proposed method enables automatic CNN model creation for exon and intron classification.

Conclusions:

The proposed optCNN model, optimized by AVOA, offers a reliable and inventive approach to exon and intron classification.
This method significantly enhances accuracy in genomic sequence analysis.
The automatic CNN model generation capability represents a novel advancement in the field.