ReviewFrontiers in systems biology2026
AI-based methods for the assessment of DNA damage and repair mechanisms.
Review in Frontiers in systems biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
In recent years, a growing number of artificial intelligence (AI)-driven approaches have been developed to elucidate chemico-biological interactions associated with DNA damage and oxidative stress. Deep learning-based techniques, in particular, have demonstrated substantial potential within molecular biology and toxicology. As a result, researchers and clinicians alike hold high expectations that AI-enabled tools will soon make meaningful contributions to our understanding of the molecular and cellular mechanisms governing DNA damage and repair. In this article, we present a concise yet comprehensive overview of the computational methodologies underpinning contemporary deep learning approaches. We examine their capacity to support DNA damage assessment by revealing mechanistic insights into damage induction and response pathways. Particular emphasis is placed on deep learning techniques designed to enhance the analysis of complex biological data, including the automated detection and quantification of DNA damage from
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