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

Journal of Biomedical Research & Environmental Sciences article abstract with citation details, DOI, publication dates, subject areas, full text links, and references.

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Open Access
Article Type Opinion
Subject Biology Group
OCLC JBRES Record
Yufeng Liu and Muhammad Riaz*
Issue: Volume7-Issue4
Pages: 1-6
Received: 2026-03-31
Accepted: 2026-04-05
Published: 2026-04-06

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.

FullText HTML FullText PDF DOI: 10.37871/jbres2289 Crossmark

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Copyright

© 2026 Liu Y et al. Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

Liu Y, Riaz M. Enhanced Genome Editing Needs an AI-Powered Meta-Platform for Smarter TALEN and CRISPR Tool Selection. J Biomed Res Environ Sci. 2026 Apr 06; 7(4): 6. Doi: 10.37872/jbres2289

Subject area(s)

BioinformaticsBioengineeringBiotechnology

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