##UbiSite-XGBoost
Prediction of protein ubiquitination sites via multi-view features based on eXtreme gradient boosting classifier
###UbiSite-XGBoost uses the following dependencies:
- Python 3.6
- numpy
- scipy
- scikit-learn
- pandas
###Guiding principles:
**The dataset file contains six Training datasets, among which Set1, Set2, Set3,Set4, Set5, and Set6, and three independent test datasets, among which Independent test1, Independent test2 , Independent test3.
**Feature extraction：
- PseAAC.py is the implementation of PseAAC.
- CKSAAP.m is the implementation of CKSAAP.
- Bi_profile_bayes.m is the implementation of ANBPB.
- AAindex.py is the implementation of implement AAindex.
- EBGW_DATA.m and EBGW.m are the implementation of EBGW.
- BLOSUM62.py is the implementation of BLOSUM62.
- zhenglinxulie.m and PsePSSM.m are the implementation of PsePSSM.
**Dimension reduction:
- LASSO_dimensional reduction.py and dimensional_reduction.py are the implementation of LASSO.
- Elastic net.py is the implementation of Elastic net.
- ET.py is the implementation of ET.
- LR.py is the implementation of LR.
- MI.py is the implementation of MI.
- SVD.py is the implementation of SVD.
**SMOTE:
- SMOTE_R_train_test.R is the implementation of SMOTE.
**Classifier:
- AdaBoost.py is the implementation of Adaboost.
- ET.py is the implementation of ET.
- GTB.py is the implementation of GTB.
- KNN.py is the implementation of KNN.
- DT.py is the implementation of DT.
- RF.py is the implementation of RF.
- SVM.py is the implementation of SVM.
- XGBoost.py is the implementation of XGBoost.
- LightGBM.py is the implementation of LightGBM.
- Bagging.py is the implementation of Bagging.