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Maya Maria Nainggolan
Maya Maria Nainggolan Mohon Tunggu... Blogger

I'm a Statistics graduate with a strong passion for Artificial Intelligence, data-driven research, and science communication. My experience spans market research, survey analysis, and project management in both media and government sectors. I write to explore complex topics like AGI, ethics, and digital innovation—making them accessible to wider audiences. Curious mind, data lover, and always ready to learn and share.

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Artificial intelligence

Revolutionizing Genomic Editing with AI

27 April 2025   00:26 Diperbarui: 27 April 2025   00:26 36
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AI and Genomic Editing - Flowchart Infographic  Source: ChatGPT/OpenAI 

I. Introduction: A New Frontier in Genetic Engineering

The fusion of Artificial Intelligence (AI) and genomic editing, particularly using CRISPR technology, is reshaping what is possible in science and medicine. By automating and optimizing complex genetic manipulations, AI is not just supporting genomic editing --- it is enhancing it beyond human capability. This partnership opens doors to faster treatments, personalized medicine, sustainable agriculture, and even solutions for genetic disorders once deemed incurable.

II. How AI Powers the Future of Genomic Editing

1. Intelligent Target Selection

One of the primary challenges in genomic editing is selecting the precise DNA sequence to modify. Traditionally, researchers manually studied genetic data, a time-consuming and error-prone process. Today, AI models, particularly Deep Learning algorithms, can scan millions of DNA sequences to find optimal editing sites with minimal risk of off-target effects.

Real Example:
DeepCRISPR, a project by MIT and Broad Institute, uses deep neural networks to predict CRISPR efficiency and guide RNA design, significantly reducing unwanted mutations.

2. Predicting Editing Outcomes

Genomic modifications can have unpredictable consequences. AI models simulate the effects of specific edits, helping researchers anticipate possible unintended effects before experiments. This reduces ethical risks, financial costs, and enhances the safety of therapies.

Real Example:
CRISPRoff is a technique developed by MIT researchers that, with AI models, enables reversible and programmable gene silencing without altering DNA sequences, minimizing permanent genetic risks.

3. Automating Data Management and Interpretation

Each CRISPR experiment generates terabytes of genetic data. AI automates the process of filtering, cleaning, and interpreting these enormous datasets, allowing scientists to find patterns and make discoveries faster than ever before.

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