Hub-centered gene network reconstruction using automatic relevance determination.

PloS One
Matthias BöckLars Kaderali

Abstract

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Netwo...Continue Reading

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Citations

May 20, 2014·Current Opinion in Gastroenterology·Christos PolytarchouDimitrios Iliopoulos
Jul 3, 2013·Cell Communication and Signaling : CCS·Panuwat TrairatphisanThomas Sauter
May 31, 2015·Bioinformatics·Marta R A MatosLars Kaderali
Nov 2, 2013·Mathematical Biosciences·George Michailidis, Florence d'Alché-Buc
Aug 5, 2016·BMC Systems Biology·Yuanfang RenTamer Kahveci
Jul 28, 2018·Journal of Bioinformatics and Computational Biology·Chi-Kan Chen
Dec 14, 2017·Frontiers in Plant Science·Satoru KodaKeiichi Mochida

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Software Mentioned

Banjo
igraph
minet
GeneNetWeaver
gnu
ARACNE
R package pROC
R package minet
JumboSF
MRNet

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