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Prediction of Extracellular Matrix Proteins by Fusing Multiple Feature Information, Elastic Net, and Random Forest Algorithm

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  • Minghui Wang

    (College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Lingling Yue

    (College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Xiaowen Cui

    (College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Cheng Chen

    (College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Hongyan Zhou

    (College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Qin Ma

    (Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA)

  • Bin Yu

    (College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
    School of Life Sciences, University of Science and Technology of China, Hefei 230027, China)

Abstract

Extracellular matrix (ECM) proteins play an important role in a series of biological processes of cells. The study of ECM proteins is helpful to further comprehend their biological functions. We propose ECMP-RF (extracellular matrix proteins prediction by random forest) to predict ECM proteins. Firstly, the features of the protein sequence are extracted by combining encoding based on grouped weight, pseudo amino-acid composition, pseudo position-specific scoring matrix, a local descriptor, and an autocorrelation descriptor. Secondly, the synthetic minority oversampling technique (SMOTE) algorithm is employed to process the class imbalance data, and the elastic net (EN) is used to reduce the dimension of the feature vectors. Finally, the random forest (RF) classifier is used to predict the ECM proteins. Leave-one-out cross-validation shows that the balanced accuracy of the training and testing datasets is 97.3% and 97.9%, respectively. Compared with other state-of-the-art methods, ECMP-RF is significantly better than other predictors.

Suggested Citation

  • Minghui Wang & Lingling Yue & Xiaowen Cui & Cheng Chen & Hongyan Zhou & Qin Ma & Bin Yu, 2020. "Prediction of Extracellular Matrix Proteins by Fusing Multiple Feature Information, Elastic Net, and Random Forest Algorithm," Mathematics, MDPI, vol. 8(2), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:169-:d:314961
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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