Improved CRISPR/Cas9 Off-target Prediction with DNABERT and Epigenetic Features
Bibliographic record
Abstract
Abstract CRISPR/Cas9 genome editing is a powerful tool in genetic engineering and gene therapy; however, off-target effects pose significant challenges for clinical applications. Accurate prediction of these unintended edits is crucial for ensuring safety and efficacy. In this study, we propose a novel approach that integrates DNABERT, a pre-trained DNA language model, with epigenetic features to improve off-target effect prediction. We evaluated DNABERT-based models against five state-of-the-art baseline models (GRU-Emb, CRISPR-BERT, CRISPR-HW, CRISPR-DIPOFF, and CrisprBERT) using four key performance metrics (F1-score, MCC, ROC-AUC, and PR-AUC). Additionally, we conducted ablation studies to assess the impact of DNABERT’s pre-training and epigenetic features, demonstrating that both significantly enhance predictive performance. Furthermore, we explored an ensemble modeling approach, which achieves superior prediction accuracy compared to individual models. Finally, we visualized DNABERT’s attention weights to gain insights into its decision-making process, revealing biologically relevant patterns in off-target recognition. The source codes used in this study are available at github.com/kimatakai/CRISPR_DNABERT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".