Machine-learning in optimization of CRISPR technology

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system has become an indispensable tool in modern gene-engineering applications over recent years. Despite its rapid adoption, further development and broader applicability are hindered by several inherent limitations. This review surveys a range of machine-learning based approaches that aim to address these challenges. In particular, we focus on the optimization of key components of the CRISPR system, including protospacer adjacent motif recognition, Cas-protein-engineering, guide RNA sequence design and extension of the approaches to alternative editing modalities. Applied machine-learning methodologies, their underlying rationale, and comparisons with experimental observations are critically discussed. Special emphasis is placed on the role of machine-learning frameworks in advancing biophysical research, where complex, high-dimensional data increasingly demand integrative computational approaches. Finally, we outline current limitations, draw overarching conclusions, and propose perspectives for future developments and applications in CRISPR-based technologies.