An Ensemble Method to Reconstruct Gene Regulatory Networks Based on Multivariate Adaptive Regression Splines.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Ruiqing ZhengJianxin Wang

Abstract

Gene regulatory networks (GRNs) play a key role in biological processes. However, GRNs are diverse under different biological conditions. Reconstructing gene regulatory networks (GRNs) from gene expression has become an important opportunity and challenge in the past decades. Although there are a lot of existing methods to infer the topology of GRNs, such as mutual information, random forest, and partial least squares, the accuracy is still low due to the noise and high dimension of the expression data. In this paper, we introduce an ensemble Multivariate Adaptive Regression Splines (MARS) based method to reconstruct the directed GRNs from multifactorial gene expression data, called PBMarsNet. PBMarsNet incorporates part mutual information (PMI) to pre-weight the candidate regulatory genes and then uses MARS to detect the nonlinear regulatory links. Moreover, we apply bootstrap to run the MARS multiple times and average the outputs of each MARS as the final score of regulatory links. The results on DREAM4 challenge and DREAM5 challenge datasets show PBMarsNet has a superior performance and generalization over other state-of-the-art methods.

Citations

May 20, 2020·Frontiers in Genetics·Ruiqing ZhengMin Li
Mar 2, 2021·Genomics, Proteomics & Bioinformatics·Zhenlan LiangJianxin Wang

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Software Mentioned

PLSNET
PBMarsNet
bLARS
AUROC
AUPR
Jump3
GENIE3
TIGRESS
GeneNetWeaver
Dialogue for Reverse Engineering Assessments and Methods ( DRE...

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