Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.

Artificial intelligence (AI) is reshaping genomics by enabling unprecedented insights into disease mechanisms, therapeutic design, and precision medicine. Integrating multi-omics data through advanced architectures, such as graph neural networks and multimodal DL promises deeper biological understanding.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 This review provides a comprehensive survey of cutting-edge AI methodologies, including machine learning, deep learning (DL), natural language processing, large language models, generative frameworks, and explainable AI, and their applications across genomics.
• 2 We systematically summarize how these technologies advance key domains, such as gene sequencing, variant detection, gene expression analysis, personalized medicine, and CRISPR-based genome editing.
• 3 Core computational tools, benchmark datasets, and open-source frameworks supporting AI-driven genomic research are detailed.

Understanding this could lead to better treatments, improved diagnostics, or a deeper grasp of how the human body works — benefiting patient care globally.

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