Graphical Gaussian Modeling for Gene Association Structures Based on Expression Deviation Patterns Induced by Various Chemical Stimuli
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.4 pp.1563-1574
- Publication Date
- 2006/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.4.1563
- Type of Manuscript
- PAPER
- Category
- Biological Engineering
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Tetsuya MATSUNO, Nobuaki TOMINAGA, Koji ARIZONO, Taisen IGUCHI, Yuji KOHARA, "Graphical Gaussian Modeling for Gene Association Structures Based on Expression Deviation Patterns Induced by Various Chemical Stimuli" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 4, pp. 1563-1574, April 2006, doi: 10.1093/ietisy/e89-d.4.1563.
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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.4.1563/_p
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@ARTICLE{e89-d_4_1563,
author={Tetsuya MATSUNO, Nobuaki TOMINAGA, Koji ARIZONO, Taisen IGUCHI, Yuji KOHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Graphical Gaussian Modeling for Gene Association Structures Based on Expression Deviation Patterns Induced by Various Chemical Stimuli},
year={2006},
volume={E89-D},
number={4},
pages={1563-1574},
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.},
keywords={},
doi={10.1093/ietisy/e89-d.4.1563},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Graphical Gaussian Modeling for Gene Association Structures Based on Expression Deviation Patterns Induced by Various Chemical Stimuli
T2 - IEICE TRANSACTIONS on Information
SP - 1563
EP - 1574
AU - Tetsuya MATSUNO
AU - Nobuaki TOMINAGA
AU - Koji ARIZONO
AU - Taisen IGUCHI
AU - Yuji KOHARA
PY - 2006
DO - 10.1093/ietisy/e89-d.4.1563
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2006
AB - 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.
ER -
English
