ArticleNature communications2022
Automated high-throughput genome editing platform with an AI learning in situ prediction model.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Silicate-bound iron drives abiotic solar energy conversion in terrestrial environments.Nature communications · 2026Article
- Fueling chromosomal gene diversification and artificial evolution with CRISPR.Genome biology · 2025Review
- Graph-CRISPR: a gene editing efficiency prediction model based on graph neural network with integrated sequence and secondary structure feature extraction.Briefings in bioinformatics · 2025Article
- Revolutionizing CRISPR technology with artificial intelligence.Experimental & molecular medicine · 2025Review
- SELECT: high-precision genome editing strategy via integration of CRISPR-Cas and DNA damage response for cross-species applications.Nucleic acids research · 2025Article
- Artificial Intelligence and Computational Biology in Gene Therapy: A Review.Biochemical genetics · 2025Review
- Big data for neuroscience in the context of predictive, preventive, and personalized medicine.The EPMA journal · 2025Review
- Integrating protein language models and automatic biofoundry for enhanced protein evolution.Nature communications · 2025Article
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- Artificial intelligence in medical science: a review.Irish journal of medical science · 2024Review
- Promising non-model microbial cell factories obtained by genome reduction.Frontiers in bioengineering and biotechnology · 2024Review
- Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions.Frontiers in bioengineering and biotechnology · 2023Review
- Automated high-throughput genome editing platform with an AI learning in situ prediction model.Nature communications · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A great number of cell disease models with pathogenic SNVs are needed for the development of genome editing based therapeutics or broadly basic scientific research. However, the generation of traditional cell disease models is heavily dependent on large-scale manual operations, which is not only time-consuming, but also costly and error-prone. In this study, we devise an automated high-throughput platform, through which thousands of samples are automatically edited within a week, providing edited cells with high efficiency. Based on the large in situ genome editing data obtained by the automatic high-throughput platform, we develop a Chromatin Accessibility Enabled Learning Model (CAELM) to predict the performance of cytosine base editors (CBEs), both chromatin accessibility and the context-sequence are utilized to build the model, which accurately predicts the result of in situ base editing. This work is expected to accelerate the development of BE-based genetic therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.