Artificial intelligence applications to genomic data in cancer research: a review of recent trends and emerging areas

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Abstract
This review focuses on the intersection of artificial intelligence and genomic data in cancer research. It explores the types of genomic data used in the literature, the methodologies of machine learning and deep learning, recent applications, and the challenges associated with this field. Through an analysis of 47,586 articles and addressing seven research questions, the study reveals significant growth in this area over the past years. While there has been remarkable progress, ongoing attention is needed to address ethical considerations, interpretability of algorithms, and potential data biases, to ensure the reliable and responsible use of these advanced technologies. Overall, this paper provides a comprehensive overview of the current research landscape, offering insights into both the potential and challenges of AI in genomic data research.

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