Protein complex identification algorithm based on XGboost and topological structural information
XU Zhoubo1, YANG Jian1, LIU Huadong1,2, HUANG Wenwen1
1.Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), GuilinGuangxi 541004, China
2.School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, GuilinGuangxi 541004, China
Large amount of uncertainty in PPI network and the incompleteness of the known protein complex data add inaccuracy to the methods only considering the topological structural information to search or performing supervised learning to the known complex data. In order to solve the problem, a search method called XGBoost model for Predicting protein complex (XGBP) was proposed. Firstly, feature extraction was performed based on the topological structural information of complexes. Then, the extracted features were trained by XGBoost model. Finally, a mapping relationship between features and protein complexes was constructed by combining topological structural information and supervised learning method, in order to improve the accuracy of protein complex prediction. Comparisons were performed with eight popular unsupervised algorithms: Markov CLustering (MCL), Clustering based on Maximal Clique (CMC), Core-Attachment based method (COACH), Fast Hierarchical clustering algorithm for functional modules discovery in Protein Interaction (HC-PIN), Cluster with Overlapping Neighborhood Expansion (ClusterONE), Molecular COmplex DEtection (MCODE), Detecting Complex based on Uncertain graph model (DCU), Weighted COACH （WCOACH）； and three supervisedmethods Bayesian Network (BN), Support Vector Machine (SVM), Regression Model (RM). The results show that the proposed algorithm has good performance in terms of precision, sensitivity and F-measure.
徐周波, 杨健, 刘华东, 黄文文. 基于XGBoost与拓扑结构信息的蛋白质复合物识别算法[J]. 计算机应用, 2020, 40(5): 1510-1514.
XU Zhoubo, YANG Jian, LIU Huadong, HUANG Wenwen. Protein complex identification algorithm based on XGboost and topological structural information. Journal of Computer Applications, 2020, 40(5): 1510-1514.
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