Improving Genomic Analysis with Convolutional, Recurrent, and Transformer Neural Networks

Abstract

The advent of deep learning has revolutionized the field of genomics, offering new ways to analyze and interpret complex genetic data. This paper introduces GenomicNet, a novel deep learning-based system designed to advance genomic analysis through a unified framework. GenomicNet integrates convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer architectures to predict the effects of genetic variants, identify regulatory elements, and model protein structures with high accuracy. Comparative evaluations show that GenomicNet outperforms existing systems like DeepSEA and AlphaFold in terms of accuracy, precision, and computational efficiency. The proposed system also incorporates advanced techniques for real-time processing and personalized medicine applications. Future enhancements will focus on integrating multi-omics data, improving interpretability, and expanding applications to population genomics and clinical settings. GenomicNet represents a significant step forward in harnessing deep learning for genomic research and personalized healthcare.