Towards evaluation of inferred gene network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S.cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions.

Original languageEnglish
Title of host publicationProceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007
Pages57-62
Number of pages6
DOIs
Publication statusPublished - 2007
Event2007 International Conference on Computational Science and its Applications, ICCSA 2007 - Kuala Lumpur
Duration: 26 Aug 200729 Aug 2007

Other

Other2007 International Conference on Computational Science and its Applications, ICCSA 2007
CityKuala Lumpur
Period26/8/0729/8/07

Fingerprint

Genes
Gene expression
Microarrays
Cells
Bioactivity
Yeast

Keywords

  • Bayesian network
  • Gene interactions
  • Gene network
  • Microarray gene expression

ASJC Scopus subject areas

  • Computer Science Applications

Cite this

Zainudin, S., & Deris, S. (2007). Towards evaluation of inferred gene network. In Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007 (pp. 57-62). [4301125] https://doi.org/10.1109/ICCSA.2007.79

Towards evaluation of inferred gene network. / Zainudin, Suhaila; Deris, S.

Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007. 2007. p. 57-62 4301125.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zainudin, S & Deris, S 2007, Towards evaluation of inferred gene network. in Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007., 4301125, pp. 57-62, 2007 International Conference on Computational Science and its Applications, ICCSA 2007, Kuala Lumpur, 26/8/07. https://doi.org/10.1109/ICCSA.2007.79
Zainudin S, Deris S. Towards evaluation of inferred gene network. In Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007. 2007. p. 57-62. 4301125 https://doi.org/10.1109/ICCSA.2007.79
Zainudin, Suhaila ; Deris, S. / Towards evaluation of inferred gene network. Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007. 2007. pp. 57-62
@inproceedings{51fea3d551314cfd9588fd9007676d71,
title = "Towards evaluation of inferred gene network",
abstract = "Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S.cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions.",
keywords = "Bayesian network, Gene interactions, Gene network, Microarray gene expression",
author = "Suhaila Zainudin and S. Deris",
year = "2007",
doi = "10.1109/ICCSA.2007.79",
language = "English",
isbn = "0769529453",
pages = "57--62",
booktitle = "Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007",

}

TY - GEN

T1 - Towards evaluation of inferred gene network

AU - Zainudin, Suhaila

AU - Deris, S.

PY - 2007

Y1 - 2007

N2 - Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S.cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions.

AB - Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S.cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions.

KW - Bayesian network

KW - Gene interactions

KW - Gene network

KW - Microarray gene expression

UR - http://www.scopus.com/inward/record.url?scp=48049119216&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=48049119216&partnerID=8YFLogxK

U2 - 10.1109/ICCSA.2007.79

DO - 10.1109/ICCSA.2007.79

M3 - Conference contribution

SN - 0769529453

SN - 9780769529455

SP - 57

EP - 62

BT - Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007

ER -