A Deep Learning and XGBoost-based Method for Predicting Protein-protein Interaction Sites

local_feature_training_set.csv: Preprocessing data of feature extractor contains 65869 rows and 344 columns, and rows represent the number of samples , the first 343 columns represent feature and the last column represent label local_feature_testing_set.csv: Preprocessing data of feature extractor contains 11791 rows and 344 columns, and rows represent the number of samples , the first 343 columns represent feature and the last column represent label global&local_feature_training_set.csv: Preprocessing data of feature extractor contains 65869 rows and 1028 columns, and rows represent the number of samples , the first 1027 columns represent feature and the last column represent label global&local_feature_testing_set.csv: Preprocessing data of feature extractor contains 11791 rows and 1028 columns, and rows represent the number of samples , the first 1027 columns represent feature and the last column represent label