Optimization of Deep Learning Convolutional Neural Network for Genomic Sequence Classification

Genomic sequence classification plays a key role in genomics by enabling the categorization of different DNA regions, including promoters, enhancers, coding, and non-coding sequences. The ability to accurately classify these regions is crucial for understanding gene regulation, genome organization, and molecular disease mechanisms. In this study, we optimized a deep learning-based classifier, a convolutional neural network (CNN), improving upon a baseline CNN model from prior research. Using benchmark datasets for genomic sequence classification, our enhanced model demonstrated superior performance in both accuracy and F1-score, validating its effectiveness for high-throughput, sequence-based genomic analysis.