ArticleGenes2025
CRISPR-MFH: A Lightweight Hybrid Deep Learning Framework with Multi-Feature Encoding for Improved CRISPR-Cas9 Off-Target Prediction.
Article in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Nucleofection-Based CRISPR/Cas Delivery in Human T Cells for Immunotherapy Applications.Methods in molecular biology (Clifton, N.J.) · 2027Article
- SNIPSNP: precision design of CRISPR/Cas9 knock-in reagents for variant correction and disease modeling.Nucleic acids research · 2026Article
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- CRISPR-MBTF: a multi-branch transformer fusion framework for CRISPR-Cas9 off-target prediction.Briefings in bioinformatics · 2026Article
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- CRISPR-FMC: a dual-branch hybrid network for predicting CRISPR-Cas9 on-target activity.Frontiers in genome editing · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe CRISPR-Cas9 system has emerged as one of the most promising gene-editing technologies in biology. However, off-target effects remain a significant challenge. While recent advances in deep learning have led to the development of models for off-target prediction, these models often fail to fully leverage sequence pair information. Furthermore, as the models' parameter sizes increase, so do their complexities, limiting their practical applicability.
methodsIn this study, we introduce a novel multi-feature independent encoding method, which encodes the gRNA-DNA sequence pair into three distinct feature matrices to minimize information loss. Additionally, we propose a lightweight hybrid deep learning framework, CRISPR-MFH, that integrates multi-scale separable convolutions and hybrid attention mechanisms for efficient and accurate off-target prediction.
resultsExtensive experiments across multiple benchmark datasets demonstrate that the proposed encoding method effectively captures critical features and that CRISPR-MFH outperforms or matches state-of-the-art models with significantly fewer parameters across multiple evaluation metrics.
conclusionsThis study offers a novel perspective for advancing deep learning technology in the realm of CRISPR-Cas9 off-target detection.
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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.