ArticlePlant molecular biology2021
sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.
Article in Plant molecular biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 109 citations in OpenAlex.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
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- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- Review
- Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.Bioinformatics (Oxford, England) · 2025Article
- DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features.Briefings in functional genomics · 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
- GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.Biology · 2024Article
- Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.BMC medical informatics and decision making · 2024Article
- Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings.Frontiers in medicine · 2023Article
- CRISPR-mediated technology for seed oil improvement in rapeseed: Challenges and future perspectives.Frontiers in plant science · 2023Review
- Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.Computational and structural biotechnology journal · 2023Review
- Deep learning in CRISPR-Cas systems: a review of recent studies.Frontiers in bioengineering and biotechnology · 2023Review
- Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites inInternational journal of molecular sciences · 2022Article
- Underlying Causes and Co-existence of Malnutrition and Infections: An Exceedingly Common Death Risk in Cancer.Frontiers in nutrition · 2022Review
- Interactions between favipiravir and a BNC cage towards drug delivery applications.Structural chemistry · 2022Article
- Cytotoxicity properties of plant-mediated synthesized K-doped ZnO nanostructures.Bioprocess and biosystems engineering · 2022Article
- CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.PLoS computational biology · 2022Article
- FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors.Computational intelligence and neuroscience · 2022Article
- CRISPR for accelerating genetic gains in under-utilized crops of the drylands: Progress and prospects.Frontiers in genetics · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageWe proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (sgRNA) plays an important role in gene redirection and editing. sgRNA has played an important role in the improvement of agronomic species, but there is a lack of effective bioinformatics tools to identify the activity of sgRNA in agronomic species. Therefore, it is necessary to develop a method based on machine learning to identify sgRNA high on-target activity. In this work, we proposed a simple convolutional neural network method to identify sgRNA high on-target activity. Our study used one-hot encoding and k-mers for sequence data conversion and a voting algorithm for constructing the convolutional neural network ensemble model sgRNACNN for the prediction of sgRNA activity. The ensemble model sgRNACNN was used for predictions in four crops: Glycine max, Zea mays, Sorghum bicolor and Triticum aestivum. The accuracy rates of the four crops in the sgRNACNN model were 82.43%, 80.33%, 78.25% and 87.49%, respectively. The experimental results showed that sgRNACNN realizes the identification of high on-target activity sgRNA of agronomic data and can meet the demands of sgRNA activity prediction in agronomy to a certain extent. These results have certain significance for guiding crop gene editing and academic research. The source code and relevant dataset can be found in the following link: https://github.com/nmt315320/sgRNACNN.git .
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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 Socratic graph.