A machine learning-based model for predicting YTHDF2 binding regions in mRNAs via sequence-based properties.
1--sys
2--numpy
3--sklearn
4--joblib
m6ABRP is implemented using Python2.7.
This script can be used for feature encoding automatically.
Usage: python model_training.py training_positive_dataset training_negative_dataset model_file scale_file pca_file
This script is used to train the m6ABRP tool.
Outputs:
1--a model file, model.pkl, which can be directly used for prediction.
2--a normalized file, normalization.pkl, which can be used to normalized the input data.
3--a pca model file, pca.pkl, which can be used to generate the principal components.
Usage: python model_indepedent_testing.py test_positive_dataset test_negative_dataset model_file scale_file pca_file
This script is used to evaluate the performance of m6ABRP on the indepedent testing dataset.
The model_file, scale_file and pca_file generated in the training process must be involved.
Outputs:
1--m6ABRP_score.txt, which consists of the prediction score of each sample.
2--test_label.txt, which includes the label of each sample.