ReviewFrontiers in bioengineering and biotechnology2023
Deep learning in CRISPR-Cas systems: a review of recent studies.
Review in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Advancements in CRISPR/Cas Technologies for Sensitive Cancer Detection: Mechanisms, Platforms, and Clinical Translation Roadmap.Diagnostics (Basel, Switzerland) · 2026Review
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- An overview of CRISPR-artificial intelligence theranostics: Current and emerging applications.Biomaterials translational · 2026Review
- Computational Methods to Engineer Cas Proteins for Efficient Genome Editing.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Review
- Review
- Recent applications, future perspectives, and limitations of the CRISPR-Cas system.Molecular therapy. Nucleic acids · 2025Review
- Engineering a New Generation of Gene Editors: Integrating Synthetic Biology and AI Innovations.ACS synthetic biology · 2025Review
- Synergizing CRISPR-Cas9 with Advanced Artificial Intelligence and Machine Learning for Precision Drug Delivery: Technological Nexus and Regulatory Insights.Current gene therapy · 2025Review
- Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets.Frontiers in nutrition · 2025Review
- Advances in WRKY regulation of immune responses in medicinal plants.Frontiers in plant science · 2025Review
- Current Knowledge on CRISPR Strategies Against Antimicrobial-Resistant Bacteria.Antibiotics (Basel, Switzerland) · 2024Review
- A comprehensive study of Z-DNA density and its evolutionary implications in birds.BMC genomics · 2024Article
- Overcoming CRISPR-Cas9 off-target prediction hurdles: A novel approach with ESB rebalancing strategy and CRISPR-MCA model.PLoS computational biology · 2024Article
- Article
- Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering.Journal of agricultural and food chemistry · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019-2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.