Enhanced Genome Editing Needs an AI-Powered Meta-Platform for Smarter TALEN and CRISPR Tool Selection

Abstract

Artificial Intelligence (AI) has become central to genome-editing design, enabling predictive modeling of TALEN- and CRISPR-based systems by integrating genomic sequence, structural features, and biological context. However, the rapid expansion of Machine Learning (ML)-driven tools has created a fragmented ecosystem, limiting systematic benchmarking, knowledge extraction, and reproducible decision-making. We propose an AI-driven meta-platform that uses meta-learning and ensemble modeling to aggregate predictions from multiple genome-editing design tools. This framework harmonizes heterogeneous datasets and incorporates continuous feedback from experimentally validated editing outcomes to evaluate model performance across diverse biological contexts. Through learning the context-dependent strengths and limitations of individual predictors, the platform enables tool-agnostic benchmarking, bias mitigation, and confidence-aware prioritization of genome-editing strategies. We argue that such an AI-powered meta-platform can transform genome editing from isolated ML models into a unified, knowledge-centric mega-platform, improving robustness, interpretability, and reproducibility while supporting responsible data governance through explainable AI and federated learning.