AIMC Topic: CRISPR-Cas Systems

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Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities.

Briefings in bioinformatics Sep 22, 2023
In silico design of single guide RNA (sgRNA) plays a critical role in clustered regularly interspaced, short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system. Continuous efforts are aimed at improving sgRNA design with efficient o...
CRISPR-Cas Systems Gene Editing RNA, Guide, CRISPR-Cas Systems Deep Learning
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Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.

Briefings in bioinformatics May 19, 2023
CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method...
Deep Learning CRISPR-Cas Systems Machine Learning Gene Editing
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CRISPR-Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning.

Nucleic acids research Apr 22, 2022
The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools ha...
RNA, Guide, CRISPR-Cas Systems CRISPR-Cas Systems Gene Editing Deep Learning
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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 Jan 25, 2022
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...
Neural Networks, Computer Algorithms Reproducibility of Results Humans Gene Targeting CRISPR-Cas Systems RNA, Guide, CRISPR-Cas Systems Deep Learning Gene Editing Genomics
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CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes.

Bioinformatics (Oxford, England) Jul 12, 2021
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, ...
Clustered Regularly Interspaced Short Palindromic Repeats Gene Editing Deep Learning CRISPR-Cas Systems RNA, Guide, CRISPR-Cas Systems Neural Networks, Computer
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Can Deep Learning Solve the Cas9 Dilemma?

The CRISPR journal Feb 1, 2021
Algorithms Deep Learning CRISPR-Cas Systems Computational Biology
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Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.

Briefings in bioinformatics Jan 18, 2021
The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures hav...
Gene Editing Genomics CRISPR-Cas Systems Machine Learning Humans Animals
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GNL-Scorer: a generalized model for predicting CRISPR on-target activity by machine learning and featurization.

Journal of molecular cell biology Dec 3, 2020
CRISPR-Cas Systems RNA, Guide, CRISPR-Cas Systems Regression Analysis Humans Bayes Theorem Software Machine Learning Models, Genetic
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Revisiting CRISPR/Cas-mediated crop improvement: Special focus on nutrition.

Journal of biosciences Jan 1, 2020
Genome editing (GE) technology has emerged as a multifaceted strategy that instantaneously popularised the mechanism to modify the genetic constitution of an organism. The clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-a...
Endonucleases Genome, Plant Gene Editing DNA Breaks, Double-Stranded CRISPR-Cas Systems Crops, Agricultural Artificial Intelligence
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The scientific events that shaped the decade.

Nature Dec 1, 2019
Big Data Gene Editing Genomics Conservation of Energy Resources Bias CRISPR-Cas Systems Microbiota Female Climate Change Infant, Newborn Environmental Policy Humans Gravitation Artificial Intelligence Social Change Physics
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