Update README.md

README.md

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-license: mit
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+---
+license: mit
+tags:
+- biology
+- CRISPR
+---
+
+
+
+
+# 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](https://github.com/bm2-lab/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   |
+
+---