AI Medical Compendium Topic

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Gene Editing

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Predicting adenine base editing efficiencies in different cellular contexts by deep learning.

Genome biology
BACKGROUND: Adenine base editors (ABEs) enable the conversion of A•T to G•C base pairs. Since the sequence of the target locus influences base editing efficiency, efforts have been made to develop computational models that can predict base editing ou...

Synthetic biology and artificial intelligence in crop improvement.

Plant communications
Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved b...

Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis.

STAR protocols
Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductanc...

High throughput variant libraries and machine learning yield design rules for retron gene editors.

Nucleic acids research
The bacterial retron reverse transcriptase system has served as an intracellular factory for single-stranded DNA in many biotechnological applications. In these technologies, a natural retron non-coding RNA (ncRNA) is modified to encode a template fo...

Engineering a New Generation of Gene Editors: Integrating Synthetic Biology and AI Innovations.

ACS synthetic biology
CRISPR-Cas technology has revolutionized biology by enabling precise DNA and RNA edits with ease. However, significant challenges remain for translating this technology into clinical applications. Traditional protein engineering methods, such as rati...

Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.

Journal of translational medicine
The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted ther...

CRISPR-MFH: A Lightweight Hybrid Deep Learning Framework with Multi-Feature Encoding for Improved CRISPR-Cas9 Off-Target Prediction.

Genes
BACKGROUND: The CRISPR-Cas9 system has emerged as one of the most promising gene-editing technologies in biology. However, off-target effects remain a significant challenge. While recent advances in deep learning have led to the development of models...

[Intelligent design of nucleic acid elements in biomanufacturing].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Nucleic acid elements are essential functional sequences that play critical roles in regulating gene expression, optimizing pathways, and enabling gene editing to enhance the production of target products in biomanufacturing. Therefore, the design an...

[Artificial intelligence-assisted design, mining, and modification of CRISPR-Cas systems].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potential in various fields, including medicine, agriculture, and industrial biotechnology. This review co...

Deep Learning-Based Classification of CRISPR Loci Using Repeat Sequences.

ACS synthetic biology
With the widespread application of the CRISPR-Cas system in gene editing and related fields, along with the increasing availability of metagenomic data, the demand for detecting and classifying CRISPR-Cas systems in metagenomic data sets has grown si...