<b>DeepB3Pred</b>

Pipeline DeepB3Pred uses the following dependencies: MATLAB2018a python 3.10 numpy scipy pandas scikit-learn catboost= 1.1.1 gc_forset xgboost-1.5.0 tensorflow=1.15.0 Keras=2.1.6 Guiding principles: The data contains a training dataset and a testing dataset. The training dataset TR_BB.fasta includes BBB_pos and BBB_neg training samples. Testing dataset TS_BB includes BBB_pos and BBB_neg testing samples Feature_Extraction: CPSR is the implementation of component protein sequence representation. MCTD is the implementation of composition-transition and distribution, and GSFE is the implementation of graphical and statistical-based feature engineering. Classifier: DeepB3Pred.py is the implementation of the proposed method to predict B3PPs and non-B3PPs.