A POSTERIOR PROBABILITY APPROACH FOR GENE REGULATORY NETWORK INFERENCE IN GENETIC PERTURBATION DATA

被引：9

作者：

Young, William Chad ^{[1
]}

Raftery, Adrian E. ^{[1
]}

Yeung, Ka Yee ^{[2
]}

机构：

[1] Univ Washington, Dept Stat, Box 354322, Seattle, WA 98195 USA

[2] Univ Washington, Inst Technol, Box 358426,1900 Commerce St, Tacoma, WA 98402 USA

来源：

MATHEMATICAL BIOSCIENCES AND ENGINEERING | 2016年 / 13卷 / 06期

基金：

美国国家卫生研究院; 爱尔兰科学基金会;

关键词：

Bayesian analysis; gene regulatory network; statistics; statistical computation; INFERRING CELLULAR NETWORKS; TIME-SERIES; EXPRESSION; MODEL; INFORMATION; CONSTRUCTION; SPARSITY; ARACNE; LASSO;

D O I：

10.3934/mbe.2016041

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.

引用

页码：1241 / 1251

页数：11

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