Supplementary data: 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 labellocal_feature_training_set.csv：该文件为特征提取器（feature extractor）的预处理训练数据集，包含65869行与344列，其中行的数量即为样本总数，前343列为特征列，最后一列为标签列。 local_feature_testing_set.csv：该文件为特征提取器的预处理测试数据集，包含11791行与344列，行的数量即为样本总数，前343列为特征列，最后一列为标签列。 global&local_feature_training_set.csv：该文件为特征提取器的预处理训练数据集，包含65869行与1028列，行的数量即为样本总数，前1027列为特征列，最后一列为标签列。 global&local_feature_testing_set.csv：该文件为特征提取器的预处理测试数据集，包含11791行与1028列，行的数量即为样本总数，前1027列为特征列，最后一列为标签列。