Computational Prediction of Off-Target Effects in CRISPR Systems CRISPR
Bibliographic record
Abstract
CRISPR/Cas gene editing technology, with its advantages of simple operation, strong specificity and high efficiency, has become an important tool in life science research and molecular breeding. However, the off-target effect has always been a key issue restricting the further application of this technology, especially in clinical and agricultural genetic improvement, and its potential risks need to be addressed urgently. In recent years, methods based on computational prediction have gradually developed into important means for identifying and reducing off-target effects, providing theoretical support and practical guidance for CRISPR experimental design and safety assessment. This article systematically reviews the CRISPR system and the molecular mechanisms underlying its off-target effects, with a focus on three mainstream computational prediction strategies: sequence aligning methods, rule and machine learning-based methods, and deep learning frameworks. The article further explores the commonly used model evaluation indicators and experimental verification methods, and demonstrates the application process of off-target prediction through a case study of the human EMX1 gene. Finally, the contributions of computational prediction methods in enhancing editing specificity were summarized, the current limitations were analyzed, and the future directions for promoting the development of this field through multimodal data integration, algorithm optimization, and preclinical safety assessment were prospected. This article aims to provide a systematic reference for subsequent research on CRISPR-based security applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".