Abstract
The rapid growth of genomic data presents new challenges and opportunities in the field of bioinformatics. Deep learning has emerged as a powerful tool for analyzing large and complex genomic datasets, offering unprecedented capabilities for identifying patterns, predicting gene functions, and understanding genetic variations. This article explores the latest advancements in deep learning techniques applied to genomic data analysis. It discusses key approaches such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, and their applications in various aspects of genomics, including gene expression analysis, variant calling, and drug discovery. The article also highlights the integration of deep learning with other emerging technologies, such as CRISPR, and its potential to revolutionize personalized medicine and genetic research..
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