Artificial Intelligence (AI) and Machine Learning (ML), especially deep learning, have revolutionized genomics and protein structure prediction, advancing precision medicine and drug discovery. This review focuses on the most widely used AI and ML algorithms including deep learning models, from early neural networks to advanced transformer architectures and Large Language Models (LLMs), are transforming our ability to interpret genomic data, predict gene function, and accurately determine protein structures and interactions. We highlight key breakthroughs such as AlphaFold and DeepBind and discuss their impact on understanding complex biological systems. Furthermore, we address the inherent connections between genomics and protein structure prediction, emphasizing how insights from one field often inform and accelerate progress in the other. We also discuss recent advancements, such as single-cell analysis using graph neural networks (e.g., scGNN). The review classifies deep learning methods (CNNs, RNNs, transformers), evaluating their strengths, limitations, and suitable applications. We also delve into the challenges, including data quality, model interpretability, and computational demands, and explore future directions, such as the integration of multi-omics data and the development of hybrid models. Future directions, such as integrating multi-omics data and developing hybrid models, aim to enhance scalability and clinical utility. This review provides insights for researchers applying AI and ML in these fields, outlining current progress and emerging opportunities.
Hein et al. (Wed,) studied this question.