ArticleBMC bioinformatics2020
CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 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
- Machine-learning in optimization of CRISPR technology.Machine learning. Health · 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
- Programmable genome engineering and gene modifications for plant biodesign.Plant communications · 2025Review
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- CRISPR/Cas9 in colorectal cancer: Revolutionizing precision oncology through genome editing and targeted therapeutics.Iranian journal of basic medical sciences · 2025Review
- Prediction of CRISPR-Cas9 on-target activity based on a hybrid neural network.Computational and structural biotechnology journal · 2025Article
- Machine learning methods for predicting guide RNA effects in CRISPR epigenome editing experiments.bioRxiv : the preprint server for biology · 2024Article
- CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction.Briefings in bioinformatics · 2024Article
- gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement.Biomolecules · 2023Review
- Review
- Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.Briefings in bioinformatics · 2023Review
- BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models.BMC bioinformatics · 2022Article
- High-throughput methods for genome editing: the more the better.Plant physiology · 2022Review
- CRISPR genome editing using computational approaches: A survey.Frontiers in bioinformatics · 2022Review
- A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.Computational and structural biotechnology journal · 2022Review
- AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity.BMC bioinformatics · 2021Article
- Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.Nature communications · 2021Article
- Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network.Entropy (Basel, Switzerland) · 2021Article
- sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.Plant molecular biology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models.
resultsIn this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target activity prediction that leverages only traditional machine learning techniques and hand-crafted features extracted from sgRNA sequences. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. Despite using only traditional machine learning methods, we have been able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines).
conclusionCRISPRpred(SEQ) has been able to convincingly beat DeepCRISPR in 3 out of 4 cell lines. We believe that by exploring further, one can design better features only using the sgRNA sequences and can come up with a better method leveraging only traditional machine learning algorithms that can fully beat the deep learning models.
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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.