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PBMarsNet: A Multivariate Adaptive Regression Splines Based Method to Reconstruct Gene Regulatory Networks

  • Siyu Zhao
  • Ruiqing Zheng
  • Xiang Chen
  • Yaohang Li
  • Fang-Xiang Wu
  • Min Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10847)

Abstract

Gene Regulatory Network (GRN) is a directed graph which describes the regulations between genes. The problem of reconstructing GRNs has been studied for decades. Most of existing methods infer the GRNs from gene expression data. Previous studies use random forest, partial least squares or other feature selection techniques to solve it. In this paper, we propose a Multivariate Adaptive Regression Splines (MARS) based method to estimate the feature importance and reconstruct the GRNs. MARS can catch the nonlinear relationships between genes. To avoid the overfitting and make the estimation robust, we apply an ensemble model of MARS based on bootstrap and weighted features by PMI (Part mutual information), called PBMarsNet. The results show that PBMarsNet performs better than the state-of-the-art methods.

Keywords

Gene Regulatory Network Gene expression MARS PCA-PMI 

Notes

Fund Sponsored

This work was supported in part by the National Natural Science Foundation of China No. 61622213, No. 61732009, No. 61772552 and No. 61728211.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Siyu Zhao
    • 1
  • Ruiqing Zheng
    • 1
  • Xiang Chen
    • 1
  • Yaohang Li
    • 3
  • Fang-Xiang Wu
    • 2
  • Min Li
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Department of Mechanical Engineering and Division of Biomedical EngineeringUniversity of SaskatchewanSaskatoonCanada
  3. 3.Department of Computer ScienceOld Dominion UniversityNorfolkUSA

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