Advanced Deep Learning Approaches for Predicting Genomic Data: A Review

Abstract

As this type of genomic data expands with an unprecedented rate, several new opportunities and challenges in terms of predictive analytics have become manifestly obvious, driving the deployment of novel computationally demanding approaches. Deep learning has developed into a transformative tool with high-dimensional and complex-data analysis capability in genomics. This review covers the new trends in deep learning approaches to genomic data prediction on tasks such as gene-expression profiling, variant calling, and disease susceptibility forecasting. We discuss the most commonly used architectures, which include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, with their strengths and weaknesses in dealing with genomic data. Key challenges, which include model interpretability, data sparsity, and the computational costs, are tackled along with possible strategies. In the end, future directions and emerging trends come out to pinpoint how deep learning is an indispensable step in order to forward genomic research and even personalized medicine.