Title
Graphical Gaussian Modeling for Gene Association Structures Based on Expression Deviation Patterns Induced by Various Chemical Stimuli
Abstract
Activity patterns of metabolic subnetworks, each of which can be regarded as a biological function module, were focused on in order to clarify biological meanings of observed deviation patterns of gene expressions induced by various chemical stimuli. We tried to infer association structures of genes by applying the multivariate statistical method called graphical Gaussian modeling to the gene expression data in a subnetwork-wise manner. It can be expected that the obtained graphical models will provide reasonable relationships between gene expressions and macroscopic biological functions. In this study, the gene expression patterns in nematodes under various conditions (stresses by chemicals such as heavy metals and endocrine disrupters) were observed using DNA microarrays. The graphical models for metabolic subnetworks were obtained from these expression data. The obtained models (independence graph) represent gene association structures of cooperativities of genes. We compared each independence graph with a corresponding metabolic subnetwork. Then we obtained a pattern that is a set of characteristic values for these graphs, and found that the pattern of heavy metals differs considerably from that of endocrine disrupters. This implies that a set of characteristic values of the graphs can representative a macroscopic biological meaning.
Year
DOI
Venue
2006
10.1093/ietisy/e89-d.4.1563
IEICE Transactions
Keywords
Field
DocType
various chemical stimuli,heavy metal,gene association structure,gene expression data,expression deviation,graphical gaussian modeling,graphical model,independence graph,metabolic subnetworks,gene expression pattern,gene expression,gene association,characteristic value,endocrine disrupters,metabolic network
Gene,Expression (mathematics),Biological system,Artificial intelligence,Pattern recognition,Algorithm,Metabolic network,Model selection,Function (biology),Gaussian,Graphical model,DNA microarray,Mathematics
Journal
Volume
Issue
ISSN
E89-D
4
1745-1361
Citations 
PageRank 
References 
3
0.52
0
Authors
5
Name
Order
Citations
PageRank
Tetsuya Matsuno131.20
Nobuaki Tominaga230.52
Koji Arizono330.52
Taisen Iguchi430.52
Yuji Kohara582.74