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31 January 2025

Optimized CNN Using African Vulture Algorithm Boosts Exon Detection Accuracy

Integrative AI technique achieves up to 97.95% accuracy for classifying protein-coding regions within DNA sequences.

Recent advancements in genomics have led to novel methodologies for detecting exons, which are the coding sequences of DNA responsible for synthesizing proteins. A new study proposes the use of an optimized convolutional neural network (CNN) integrated with the African Vulture Optimization Algorithm (AVOA) to significantly improve the accuracy of exon detection.

Exons are the segments of DNA sequences retained during the splicing process, interspersed with introns, which are non-coding regions. Accurate identification of exons is pivotal for advancing our knowledge of genetics, as it contributes to the accuracy of genetic disease diagnosis and facilitates drug design processes. Conventional methods of exon detection have often faced limitations, prompting the need for more reliable computational models.

This study, leading the charge, introduces the optimized CNN known as optCNN, which leverages the AVOA to fine-tune the architecture and hyperparameters of the CNN model. The researchers achieved remarkable success rates of 97.95% accuracy using the GENSCAN training set and 95.39% with the HMR195 dataset.

The AVOA distinguishes itself as a cutting-edge metaheuristic inspired by the foraging behaviors of African vultures, which offers agility and speed for optimization tasks. By facilitating the optimized architecture for the CNN model, the AVOA algorithm enabled enhanced identification of the three-base periodicity characteristic of coding sequences, which is pivotal for their classification.

The research delineates the rigorous methodology involved, comprising the numerical mapping of DNA sequences and the extraction of relevant features using modified Gabor-wavelet transforms (MGWT). These processes are integral for making genomic data amenable to CNN analysis. The study emphasizes the importance of hyperparameter optimization, as the right configuration of CNN layers significantly influences performance outcomes.

Through comparative analyses, the proposed AVOA-optCNN method has demonstrated superior results when juxtaposed against traditional optimization algorithms like Particle Swarm Optimization (PSO) and Educational Competition Optimizer (ECO). The accuracy of the model showcases the computational prowess of combining biology with sophisticated AI methods.

“This innovative computational approach not only enhances the accuracy of exon detection but also opens avenues for future applications across other biological classifications. Our findings have the potential to significantly impact genomic research and precision medicine,” the authors remarked, reflecting the transformative potential of their experimental design.

Overall, this study signals a major leap forward not only for machine learning applications but also for real-world genetic research, offering pathways for accurately mapping exon locations within complex genomic structures.