CRISPR Efficiency Predictor
A deep learning model for predicting CRISPR-Cas9 editing efficiency based on DNA sequences and epigenetic features. This model integrates sequence data and epigenetic signals to provide highly accurate predictions of CRISPR editing efficiency.
Model Details
- Model Type: Convolutional Neural Network (CNN)
- Input Features:
- DNA Sequence: 23-base target sequence, one-hot encoded.
- Epigenetic Features:
- CTCF (Transcription factor binding sites)
- DNase (Chromatin accessibility)
- H3K4me3 (Histone modification marker)
- RRBS (Methylation marker)
- Output: A single efficiency score indicating the likelihood of successful CRISPR editing for the given input.
Training and Evaluation
Training Details
- Dataset: DeepCRISPR
- Citation: Guohui Chuai, Qi Liu et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. 2018 (Manuscript submitted).
- Framework: TensorFlow/Keras
- Optimizer: Adam
- Loss Function: Mean Squared Error (MSE)
Evaluation Metrics
| Metric | Value |
|---|---|
| R-squared (R²) | 0.9754 |
| Pearson Correlation Coefficient | 0.9876 |
| Mean Residual | -0.0003 |
| Residual Standard Deviation | 0.0032 |
Inference Providers
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