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Updated: Jun 16, 2026
09:43
Breeding by Design for Functional Rice with Genome Editing Technologies
Published on: January 3, 2025
The evolution of AI-integrated genome editing and its challenges.
Matthew Tze Jian Wong1, Nur Diyana Zulkifli1, Thurgashini Ravichandran2
1Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Penang, Malaysia.
Summary
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing genome editing. These tools enhance precision, predict outcomes, and accelerate therapeutic development for broader accessibility.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Genome editing technologies like CRISPR are advancing rapidly.
- Integrating Artificial Intelligence (AI) and Machine Learning (ML) offers new potential.
- AI/ML can analyze complex genomic data and improve editing processes.
Purpose of the Study:
- To explore the advancements and applications of AI in genome editing.
- To discuss the limitations and future opportunities of AI in this field.
- To highlight how AI enhances precision, efficiency, and accessibility in genome editing.
Main Methods:
- Review of AI/ML applications in genome editing design and simulation.
- Analysis of AI's role in target identification and off-target effect prediction.
- Examination of AI's impact on drug discovery and therapeutic development.
Main Results:
- AI/ML improves CRISPR system design, target identification, and off-target prediction.
- AI tools enable rapid simulation of genetic modifications and phenotype prediction.
- AI accelerates drug discovery and optimizes gene therapies through streamlined target identification.
Conclusions:
- AI/ML integration enhances every stage of genome editing, increasing precision and efficiency.
- AI-powered tools democratize genome editing, requiring less technical expertise.
- The synergy of AI and genome editing holds significant promise for future research and therapeutics.
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