ArticleFrontiers in genetics2019
A Novel Hybrid CNN-SVR for CRISPR/Cas9 Guide RNA Activity Prediction.
Article in Frontiers in genetics, 2019. 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.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- A transfer learning-based hybrid deep- and machine-learning regression approach for predicting the postoperative pulmonary function.European radiology experimental · 2026Article
- 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
- Review
- Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction.PLoS computational biology · 2025Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Engineering a New Generation of Gene Editors: Integrating Synthetic Biology and AI Innovations.ACS synthetic biology · 2025Review
- DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features.Briefings in functional genomics · 2025Article
- Synergizing CRISPR-Cas9 with Advanced Artificial Intelligence and Machine Learning for Precision Drug Delivery: Technological Nexus and Regulatory Insights.Current gene therapy · 2025Review
- Prediction of CRISPR-Cas9 on-target activity based on a hybrid neural network.Computational and structural biotechnology journal · 2025Article
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Codon usage and expression-based features significantly improve prediction of CRISPR efficiency.NPJ systems biology and applications · 2024Article
- Strong association between genomic 3D structure and CRISPR cleavage efficiency.PLoS computational biology · 2024Article
- CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.International journal of molecular sciences · 2024Article
- Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering.Nucleic acids research · 2023Article
- A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets.Nature communications · 2023Article
- Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.Briefings in bioinformatics · 2023Review
- Deep learning in CRISPR-Cas systems: a review of recent studies.Frontiers in bioengineering and biotechnology · 2023Review
- Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics.Journal of translational medicine · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of guide RNA (gRNA) on-target efficacy is critical for effective application of CRISPR/Cas9 system. Although some machine learning-based and convolutional neural network (CNN)-based methods have been proposed, prediction accuracy remains to be improved. Here, firstly we improved architectures of current CNNs for predicting gRNA on-target efficacy. Secondly, we proposed a novel hybrid system which combines our improved CNN with support vector regression (SVR). This CNN-SVR system is composed of two major components: a merged CNN as the front-end for extracting gRNA feature and an SVR as the back-end for regression and predicting gRNA cleavage efficiency. We demonstrate that CNN-SVR can effectively exploit features interactions from feed-forward directions to learn deeper features of gRNAs and their corresponding epigenetic features. Experiments on commonly used datasets show that our CNN-SVR system outperforms available state-of-the-art methods in terms of prediction accuracy, generalization, and robustness. Source codes are available at https://github.com/Peppags/CNN-SVR.
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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.