Inference of gene regulatory networks using Bayesian nonparametric regression and topology information
type: Item
instance of: scholarly article
title: Inference of gene regulatory networks using Bayesian nonparametric regression and topology information
zbMATH Open document ID: 1368.92050
DOI: 10.1155/2017/8307530
author: Yue Fan
author: Xiao Wang
author: Qinke Peng
published in: Computational \& Mathematical Methods in Medicine
publication date: 2017-08-10T00:00:00Z
review text: Summary: Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.
Mathematics Subject Classification ID: 92C40
Mathematics Subject Classification ID: 92C42
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62J20
zbMATH DE Number: 6759176
zbMATH Keywords: gene regulatory networks
zbMATH Keywords: B-spline basis functions
zbMATH Keywords: Bayesian nonparametric regression
Wikidata QID: Q37591510
describes a project that uses: ARACNE
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/8307530
OpenAlex ID: W2570295517
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DBLP publication ID: journals/cmmm/FanWP17
description: scientific article; zbMATH DE number 6759176