Deep Learning in Neural Networks and their Application in Genomics

Abstract

Deep learning has emerged as a powerful tool in genomics, utilizing neural networks to uncover complex patterns in large datasets. This review explores the application of deep learning in genomics, focusing on supervised and unsupervised learning tasks.The process involves training models with appropriate evaluation metrics and curated datasets to optimize performance. Balancing training data and model flexibility is crucial to avoid underfitting or overfitting. Deep learning models, with their high capacity and flexibility, outperform traditional techniques like logistic regression and support vector machines in genomics. Various applications of deep learning in genomics are includes predicting protein sequence specificity, determining cis-regulatory elements, analyzing splicing regulation and gene expression, and predicting genomic variants. Deep learning proves particularly effective in studying functional genomics and regulatory elements, leveraging techniques from computer vision and natural language processing. Overall, deep learning shows promise in advancing genomics research and understanding complex biological processes.