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Bayesian nonlinear model selection for gene regulatory networks
Author(s) -
Ni Yang,
Stingo Francesco C.,
Baladandayuthapani Veerabhadran
Publication year - 2015
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12309
Subject(s) - overfitting , gene regulatory network , computer science , nonlinear system , model selection , graphical model , selection (genetic algorithm) , biological network , bayesian network , artificial intelligence , machine learning , bayesian probability , flexibility (engineering) , smoothing , linear model , data mining , mathematical optimization , mathematics , computational biology , gene , artificial neural network , biology , gene expression , statistics , biochemistry , physics , quantum mechanics , computer vision
Summary Gene regulatory networks represent the regulatory relationships between genes and their products and are important for exploring and defining the underlying biological processes of cellular systems. We develop a novel framework to recover the structure of nonlinear gene regulatory networks using semiparametric spline‐based directed acyclic graphical models. Our use of splines allows the model to have both flexibility in capturing nonlinear dependencies as well as control of overfitting via shrinkage, using mixed model representations of penalized splines. We propose a novel discrete mixture prior on the smoothing parameter of the splines that allows for simultaneous selection of both linear and nonlinear functional relationships as well as inducing sparsity in the edge selection. Using simulation studies, we demonstrate the superior performance of our methods in comparison with several existing approaches in terms of network reconstruction and functional selection. We apply our methods to a gene expression dataset in glioblastoma multiforme, which reveals several interesting and biologically relevant nonlinear relationships.