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Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

BACKGROUND: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells...

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Autores principales: Kojima, Kaname, Imoto, Seiya, Yamaguchi, Rui, Fujita, André, Yamauchi, Mai, Gotoh, Noriko, Miyano, Satoru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3587380/
https://www.ncbi.nlm.nih.gov/pubmed/22369122
http://dx.doi.org/10.1186/1471-2164-13-S1-S6
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author Kojima, Kaname
Imoto, Seiya
Yamaguchi, Rui
Fujita, André
Yamauchi, Mai
Gotoh, Noriko
Miyano, Satoru
author_facet Kojima, Kaname
Imoto, Seiya
Yamaguchi, Rui
Fujita, André
Yamauchi, Mai
Gotoh, Noriko
Miyano, Satoru
author_sort Kojima, Kaname
collection PubMed
description BACKGROUND: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. METHODS: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. RESULTS: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. CONCLUSIONS: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.
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spelling pubmed-35873802013-03-11 Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing Kojima, Kaname Imoto, Seiya Yamaguchi, Rui Fujita, André Yamauchi, Mai Gotoh, Noriko Miyano, Satoru BMC Genomics Proceedings BACKGROUND: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. METHODS: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. RESULTS: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. CONCLUSIONS: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib. BioMed Central 2012-01-17 /pmc/articles/PMC3587380/ /pubmed/22369122 http://dx.doi.org/10.1186/1471-2164-13-S1-S6 Text en Copyright ©2012 Kojima et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Kojima, Kaname
Imoto, Seiya
Yamaguchi, Rui
Fujita, André
Yamauchi, Mai
Gotoh, Noriko
Miyano, Satoru
Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
title Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
title_full Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
title_fullStr Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
title_full_unstemmed Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
title_short Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
title_sort identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3587380/
https://www.ncbi.nlm.nih.gov/pubmed/22369122
http://dx.doi.org/10.1186/1471-2164-13-S1-S6
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