AI Medical Compendium Topic

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Gene Editing

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Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform.

SLAS technology
Single-cell delivery platforms like microinjection and nanoprobe electroporation enable unparalleled control over cell manipulation tasks but are generally limited in throughput. Here, we present an automated single-cell electroporation system capabl...

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

PLoS computational biology
Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments ...

[Construction of Escherichia coli cell factories].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
As an important model industrial microorganism, Escherichia coli has been widely used in pharmaceutical, chemical industry and agriculture. In the past 30 years, a variety of new strategies and techniques, including artificial intelligence, gene edit...

Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.

Nature communications
The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. ...

CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes.

Bioinformatics (Oxford, England)
MOTIVATION: CRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 editing outcomes depend on local DNA sequences at the target site and are thus predictable. However, ...

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Nature communications
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great ...

Easy-Prime: a machine learning-based prime editor design tool.

Genome biology
Prime editing is a revolutionary genome-editing technology that can make a wide range of precise edits in DNA. However, designing highly efficient prime editors (PEs) remains challenging. We develop Easy-Prime, a machine learning-based program traine...

Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.

Nature communications
Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the seq...

Uncertainty-aware and interpretable evaluation of Cas9-gRNA and Cas12a-gRNA specificity for fully matched and partially mismatched targets with Deep Kernel Learning.

Nucleic acids research
The choice of guide RNA (gRNA) for CRISPR-based gene targeting is an essential step in gene editing applications, but the prediction of gRNA specificity remains challenging. Lack of transparency and focus on point estimates of efficiency disregarding...