Estimating genome-wide gene networks using nonparametric bayesian network models on massively parallel computers

Yoshinori Tamada, Seiya Imoto, Hiromitsu Araki, Masao Nagasaki, Cristin Print, D. Stephen Charnock-Jones, Satoru Miyano

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

Original languageEnglish
Article number5551118
Pages (from-to)683-697
Number of pages15
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume8
Issue number3
DOIs
Publication statusPublished - Mar 25 2011
Externally publishedYes

Fingerprint

Gene Networks
Gene Regulatory Networks
Bayesian networks
Bayesian Model
Parallel Computers
Bayesian Networks
Network Model
Genome
Genes
Gene
Endothelial Cells
Veins
Gene Expression Data
Microarray Data
Network Structure
Regulator
Heuristic algorithm
Human Umbilical Vein Endothelial Cells
Human Genome
Regulator Genes

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics

Cite this

Estimating genome-wide gene networks using nonparametric bayesian network models on massively parallel computers. / Tamada, Yoshinori; Imoto, Seiya; Araki, Hiromitsu; Nagasaki, Masao; Print, Cristin; Charnock-Jones, D. Stephen; Miyano, Satoru.

In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 8, No. 3, 5551118, 25.03.2011, p. 683-697.

Research output: Contribution to journalArticle

Tamada, Yoshinori ; Imoto, Seiya ; Araki, Hiromitsu ; Nagasaki, Masao ; Print, Cristin ; Charnock-Jones, D. Stephen ; Miyano, Satoru. / Estimating genome-wide gene networks using nonparametric bayesian network models on massively parallel computers. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2011 ; Vol. 8, No. 3. pp. 683-697.
@article{fc45f5e2821e498f96772f9460decc26,
title = "Estimating genome-wide gene networks using nonparametric bayesian network models on massively parallel computers",
abstract = "We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.",
author = "Yoshinori Tamada and Seiya Imoto and Hiromitsu Araki and Masao Nagasaki and Cristin Print and Charnock-Jones, {D. Stephen} and Satoru Miyano",
year = "2011",
month = "3",
day = "25",
doi = "10.1109/TCBB.2010.68",
language = "English",
volume = "8",
pages = "683--697",
journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
issn = "1545-5963",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Estimating genome-wide gene networks using nonparametric bayesian network models on massively parallel computers

AU - Tamada, Yoshinori

AU - Imoto, Seiya

AU - Araki, Hiromitsu

AU - Nagasaki, Masao

AU - Print, Cristin

AU - Charnock-Jones, D. Stephen

AU - Miyano, Satoru

PY - 2011/3/25

Y1 - 2011/3/25

N2 - We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

AB - We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

UR - http://www.scopus.com/inward/record.url?scp=79952856971&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952856971&partnerID=8YFLogxK

U2 - 10.1109/TCBB.2010.68

DO - 10.1109/TCBB.2010.68

M3 - Article

C2 - 20714027

AN - SCOPUS:79952856971

VL - 8

SP - 683

EP - 697

JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics

JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics

SN - 1545-5963

IS - 3

M1 - 5551118

ER -