The notebook Importance and Efficiency Prediction.ipynb loads the test data: x_test.csv (encoded features), y_test.csv (target efficiencies), and the trained model xgb, to compute the importance of chromatin accessibility parameter relative to the context-sequence, and predict the efficiency of the test data (dummy encoded, i.e. the following representation denotes that the first base is C).
| 1_A | 1_C | 1_G | 1_T |
|---|---|---|---|
| 0 | 1 | 0 | 0 |
Software dependencies
- pandas == 1.0.5
- joblib == 0.16.0
- scipy == 1.5.0
- matplotlib == 3.2.2
- seaborn == 0.11.1
OS Requirements
- The package has been tested on macOS Big Sur Version 11.5.2.
- Install python packages: change to the directory where requirements.txt is located and run
pip install -r requirements.txt- Run the demo Jupyter Notebook Importance and Efficiency Prediction.ipynb.
- It would take 1 to 2 mins to get the outputs.