ArticleBiomolecules2023
Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 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
- Minimizing Off-Target Effects of CRISPR-Cas9 With Optimized sgRNA: Evaluation of Efficiency and Specificity in the Tumor Protein 53 (TP53) Region.Biotechnology and bioengineering · 2026Article
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- graphB3-an interpretable graph learning approach for predicting blood-brain barrier permeability.Briefings in bioinformatics · 2025Article
- siRNA Features-Automated Machine Learning of 3D Molecular Fingerprints and Structures for Therapeutic Off-Target Data.International journal of molecular sciences · 2025Article
- Emerging applications of gene editing technologies for the development of climate-resilient crops.Frontiers in genome editing · 2025Review
- Crispr-SGRU: Prediction of CRISPR/Cas9 Off-Target Activities with Mismatches and Indels Using Stacked BiGRU.International journal of molecular sciences · 2024Article
- Improved Machine Learning-Based Model for the Classification of Off-Targets in the CRISPR/Cpf1 System.ACS omega · 2023Article
- Deep learning in CRISPR-Cas systems: a review of recent studies.Frontiers in bioengineering and biotechnology · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CRISPR/Cas9 technology is capable of precisely editing genomes and is at the heart of various scientific and medical advances in recent times. The advances in biomedical research are hindered because of the inadvertent burden on the genome when genome editors are employed-the off-target effects. Although experimental screens to detect off-targets have allowed understanding the activity of Cas9, that knowledge remains incomplete as the rules do not extrapolate well to new target sequences. Off-target prediction tools developed recently have increasingly relied on machine learning and deep learning techniques to reliably understand the complete threat of likely off-targets because the rules that drive Cas9 activity are not fully understood. In this study, we present a count-based as well as deep-learning-based approach to derive sequence features that are important in deciding on Cas9 activity at a sequence. There are two major challenges in off-target determination-the identification of a likely site of Cas9 activity and the prediction of the extent of Cas9 activity at that site. The hybrid multitask CNN-biLSTM model developed, named CRISP-RCNN, simultaneously predicts off-targets and the extent of activity on off-targets. Employing methods of integrated gradients and weighting kernels for feature importance approximation, analysis of nucleotide and position preference, and mismatch tolerance have been performed.
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