ArticleComputational and structural biotechnology journal2025
Prediction of CRISPR-Cas9 on-target activity based on a hybrid neural network.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Advanced gene editing technologies for oncology mechanisms, applications, and clinical implementation.Cancer gene therapy · 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
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Computation and deep-learning-driven advances in CRISPR genome editing.Nature structural & molecular biology · 2026Review
- Review
- CRISPR-FMC: a dual-branch hybrid network for predicting CRISPR-Cas9 on-target activity.Frontiers in genome editing · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CRISPR-Cas9 is a groundbreaking gene editing technology, but variations in targeted editing efficiency arise due to significant discrepancies in sgRNA activity. Therefore, improving the prediction accuracy of sgRNA activity is crucial for its safety and effectiveness. Deep learning methods have surpassed traditional scoring and machine learning methods, demonstrating higher prediction accuracy and scalability. However, challenges persist in local feature extraction, cross-sequence dependency modeling, and dynamic feature weight assignment. To address these issues, we introduce CRISPR_HNN, a hybrid deep neural network model that integrates MSC, MHSA, and BiGRU to effectively capture local dynamic features and global long-distance dependencies. In addition, it adopts One-hot Encoding and Label Encoding strategies. Experimental results demonstrate that CRISPR_HNN surpasses existing models on public datasets and substantially enhances the accuracy of sgRNA activity prediction.
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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.