How to infer gene networks from expression profiles, revisited

被引:114
作者
Penfold, Christopher A. [1 ]
Wild, David L. [1 ]
机构
[1] Univ Warwick, Syst Biol Ctr, Coventry CV4 7AL, W Midlands, England
基金
英国工程与自然科学研究理事会; 英国生物技术与生命科学研究理事会;
关键词
gene-regulatory networks; inference; gene expression; REGULATORY NETWORKS; COMPOUND-MODE; RECONSTRUCTION; ALGORITHM; CLOCK;
D O I
10.1098/rsfs.2011.0053
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance of these competing approaches have previously been assessed using a variety of in silico and in vivo datasets. Here, we revisit this work by assessing the performance of more recent network inference algorithms, including a novel non-parametric learning approach based upon nonlinear dynamical systems. For larger GRNs, containing hundreds of genes, these non-parametric approaches more accurately infer network structures than do traditional approaches, but at significant computational cost. For smaller systems, DBNs are competitive with the non-parametric approaches with respect to computational time and accuracy, and both of these approaches appear to be more accurate than Granger causality-based methods and those using simple ODEs models.
引用
收藏
页码:857 / 870
页数:14
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