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    Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis.

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    Type
    Article
    Authors
    Wang, Xue
    Zhang, Yaqun
    Yu, Bin cc
    Salhi, Adil
    Chen, Ruixin
    Wang, Lin
    Liu, Zengfeng
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2021-06-01
    Online Publication Date
    2021-06-01
    Print Publication Date
    2021-07
    Embargo End Date
    2022-06-13
    Submitted Date
    2020-12-29
    Permanent link to this record
    http://hdl.handle.net/10754/669594
    
    Metadata
    Show full item record
    Abstract
    Predicting protein-protein interaction sites (PPI sites) can provide important clues for understanding biological activity. Using machine learning to predict PPI sites can mitigate the cost of running expensive and time-consuming biological experiments. Here we propose PPISP-XGBoost, a novel PPI sites prediction method based on eXtreme gradient boosting (XGBoost). First, the characteristic information of protein is extracted through the pseudo-position specific scoring matrix (PsePSSM), pseudo-amino acid composition (PseAAC), hydropathy index and solvent accessible surface area (ASA) under the sliding window. Next, these raw features are preprocessed to obtain more optimal representations in order to achieve better prediction. In particular, the synthetic minority oversampling technique (SMOTE) is used to circumvent class imbalance, and the kernel principal component analysis (KPCA) is applied to remove redundant characteristics. Finally, these optimal features are fed to the XGBoost classifier to identify PPI sites. Using PPISP-XGBoost, the prediction accuracy on the training dataset Dset186 reaches 85.4%, and the accuracy on the independent validation datasets Dtestset72, PDBtestset164, Dset_448 and Dset_355 reaches 85.3%, 83.9%, 85.8% and 85.4%, respectively, which all show an increase in accuracy against existing PPI sites prediction methods. These results demonstrate that the PPISP-XGBoost method can further enhance the prediction of PPI sites.
    Citation
    Wang, X., Zhang, Y., Yu, B., Salhi, A., Chen, R., Wang, L., & Liu, Z. (2021). Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis. Computers in Biology and Medicine, 134, 104516. doi:10.1016/j.compbiomed.2021.104516
    Sponsors
    This work was supported by the National Natural Science Foundation of China (No. 61863010), the Key Research and Development Program of Shandong Province of China (No. 2019GGX101001), and the Key Laboratory Open Foundation of Hainan Province (No. JSKX202001).
    Publisher
    Elsevier BV
    Journal
    Computers in biology and medicine
    DOI
    10.1016/j.compbiomed.2021.104516
    PubMed ID
    34119922
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0010482521003103
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.compbiomed.2021.104516
    Scopus Count
    Collections
    Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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