ReviewMethods in molecular biology (Clifton, N.J.)2025
Artificial Intelligence and Chromothripsis.
Review in Methods in molecular biology (Clifton, N.J.), 2025. 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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7 authors.
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Abstract
Artificial intelligence (AI) emerges as a new alternative in the healthcare domain, particularly in genomics, promising to revolutionize medicine with its various applications. In this chapter, we explore the potential of AI in genetics and research, with a particular focus on its role in the detection and characterization of chromothripsis. Chromothripsis, marked by complex genomic rearrangements, presents significant challenges in detection and interpretation. Traditional methods such as karyotyping, FISH, array-CGH, and NGS have limitations in accurately identifying chromothripsis events. However, recent advances in AI, including deep learning and machine learning, offer promising opportunities to overcome these challenges. By utilizing machine learning and deep learning algorithms, researchers can analyze complex genomic datasets, identify recurrent patterns, and predict functional consequences associated with chromothripsis with unprecedented accuracy. Additionally, AI facilitates the integration of multi-omics data, enabling a holistic understanding of chromothripsis in various pathological contexts. Through case studies and recent advancements, we highlight the potential of AI in advancing our understanding of chromothripsis and its clinical applications.
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