dc.contributor.author |
Imoto, S |
en |
dc.contributor.author |
Tamada, Y |
en |
dc.contributor.author |
Araki, H |
en |
dc.contributor.author |
Yasuda, K |
en |
dc.contributor.author |
Print, Cristin |
en |
dc.contributor.author |
Charnock-Jones, DS |
en |
dc.contributor.author |
Sanders, D |
en |
dc.contributor.author |
Savoie, C |
en |
dc.contributor.author |
Tashiro, K |
en |
dc.contributor.author |
Kuhara, S |
en |
dc.contributor.author |
Miyano, S |
en |
dc.coverage.spatial |
Maui, Hawaii |
en |
dc.date.accessioned |
2012-04-16T04:36:16Z |
en |
dc.date.issued |
2006 |
en |
dc.identifier.citation |
Pacific Symposium on Biocomputing 2006, Maui, Hawaii, 03 Jan 2006 - 07 Jan 2007. Pacific Symposium on Biocomputing Online Proceedings 2006. 11: 559-571. 2006 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/17233 |
en |
dc.description.abstract |
We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-α, which is a known target of fenofibrate. |
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dc.relation.ispartof |
Pacific Symposium on Biocomputing 2006 |
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dc.relation.ispartofseries |
Pacific Symposium on Biocomputing Online Proceedings 2006 |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.title |
Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles |
en |
dc.type |
Conference Item |
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pubs.begin-page |
559 |
en |
pubs.volume |
11 |
en |
dc.rights.holder |
Copyright: the author |
en |
pubs.author-url |
http://psb.stanford.edu/psb-online/proceedings/psb06/ |
en |
pubs.end-page |
571 |
en |
pubs.finish-date |
2007-01-07 |
en |
pubs.publication-status |
Published |
en |
pubs.start-date |
2006-01-03 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
261645 |
en |
pubs.org-id |
Medical and Health Sciences |
en |
pubs.org-id |
Medical Sciences |
en |
pubs.org-id |
Molecular Medicine |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
Science Research |
en |
pubs.org-id |
Maurice Wilkins Centre (2010-2014) |
en |
pubs.record-created-at-source-date |
2011-12-15 |
en |