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Related Concept Videos

CRISPR01:59

CRISPR

Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced Short...
CRISPR and crRNAs02:53

CRISPR and crRNAs

Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...
CRISPR01:59

CRISPR

Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced Short...
CRISPR/Cas9 Genome Editing01:28

CRISPR/Cas9 Genome Editing

The CRISPR-Cas system serves as a bacterial defense mechanism against invading genetic elements such as viruses and plasmids, forming the foundation for its adaptation as a powerful genome-editing tool. Originally discovered in prokaryotes, this system has been repurposed to revolutionize genetic engineering across a wide range of organisms, including plants, animals, and humans. The core component, Cas9, is an endonuclease derived from Streptococcus pyogenes, capable of introducing...

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Related Experiment Video

Updated: Jun 9, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
09:05

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.

Yuxi Zhao1, Xingrui Li2, Yaying Du3

  • 1Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China.

Journal of Translational Medicine
|February 20, 2026
PubMed
Summary

Artificial intelligence (AI) is revolutionizing CRISPR screening by enhancing experimental design and analysis. Machine learning and large language models create intelligent systems for more robust and scalable functional genomics discovery.

Keywords:
CRISPR screeningDeep learningExperimental design and automationFunctional genomicsGenomic editingHuman-in-the-loop systemIntelligent agentsLarge language modelsMachine learning

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Last Updated: Jun 9, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
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Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes
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Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes

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Area of Science:

  • Functional genomics
  • CRISPR screening
  • Artificial intelligence in biology

Background:

  • CRISPR screening is vital for understanding gene function and interactions.
  • Expanding screening methods reveal limitations in current experimental design and analysis.

Purpose of the Study:

  • To review how AI is transforming CRISPR screening.
  • To highlight AI's role in optimizing experimental design and analysis.

Main Methods:

  • Machine learning and deep learning (ML/DL) for optimizing experimental design.
  • Large language model-agent (LLM-agent) systems for scientific reasoning and analysis coordination.
  • Integration of AI across the CRISPR screening experimental lifecycle.

Main Results:

  • ML/DL methods improve prediction of perturbation behavior and enable efficient screening.
  • LLM-agents integrate biological knowledge and coordinate human-in-the-loop workflows.
  • AI introduces predictive, adaptive, and system-level intelligence to CRISPR screening.

Conclusions:

  • AI reframes CRISPR screening into an intelligent experimental system.
  • ML/DL and LLM-agent approaches enhance robustness, scalability, and biological discovery in CRISPR screening.