AI Approach Enables Gene-Editing Tool Optimization Under Data Constraints
Gene-editing technologies like CRISPR have transformed molecular biology, but optimizing these tools often requires extensive experimental data that can be costly and time-consuming to generate. A team at the University of Pennsylvania has shown that AI can bridge this gap, enabling meaningful improvements to gene-editing systems even under data-sparse conditions.