File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/978-3-540-28651-6_3
- Scopus: eid_2-s2.0-33746258589
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Building genetic networks for gene expression patterns
Title | Building genetic networks for gene expression patterns |
---|---|
Authors | |
Issue Date | 2004 |
Publisher | Springer. |
Citation | 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), Exeter, UK, 25-27 August 2004. In Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004: Proceedings, 2004, p. 17-24 How to Cite? |
Abstract | Building genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been developed as a model of gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and uncover the relative sensitivity of genes in their interactions with other genes. However, PBNs are unlikely used in practice because of huge number of possible predictors and their computed probabilities. In this paper, we propose a multivariate Markov chain model to govern the dynamics of a genetic network for gene expression patterns. The model preserves the strength of PBNs and reduce the complexity of the networks. Parameters of the model are quadratic with respect to the number of genes. We also develop an efficient estimation method for the model parameters. Simulation results on yeast data are given to illustrate the effectiveness of the model. © Springer-Verlag Berlin Heidelberg 2004. |
Persistent Identifier | http://hdl.handle.net/10722/156166 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.249 |
Series/Report no. | Lecture Notes in Computer Science ; 3177 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ching, WK | en_US |
dc.contributor.author | Fung, ES | en_US |
dc.contributor.author | Ng, MK | en_US |
dc.date.accessioned | 2012-08-08T08:40:40Z | - |
dc.date.available | 2012-08-08T08:40:40Z | - |
dc.date.issued | 2004 | en_US |
dc.identifier.citation | 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), Exeter, UK, 25-27 August 2004. In Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004: Proceedings, 2004, p. 17-24 | en_US |
dc.identifier.isbn | 978-3-540-22881-3 | - |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/156166 | - |
dc.description.abstract | Building genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been developed as a model of gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and uncover the relative sensitivity of genes in their interactions with other genes. However, PBNs are unlikely used in practice because of huge number of possible predictors and their computed probabilities. In this paper, we propose a multivariate Markov chain model to govern the dynamics of a genetic network for gene expression patterns. The model preserves the strength of PBNs and reduce the complexity of the networks. Parameters of the model are quadratic with respect to the number of genes. We also develop an efficient estimation method for the model parameters. Simulation results on yeast data are given to illustrate the effectiveness of the model. © Springer-Verlag Berlin Heidelberg 2004. | en_US |
dc.language | eng | en_US |
dc.publisher | Springer. | en_US |
dc.relation.ispartof | Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004: Proceedings | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 3177 | - |
dc.title | Building genetic networks for gene expression patterns | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Ching, WK:wching@hku.hk | en_US |
dc.identifier.email | Ng, KP: kkpong@hkusua.hku.hk | - |
dc.identifier.authority | Ching, WK=rp00679 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1007/978-3-540-28651-6_3 | - |
dc.identifier.scopus | eid_2-s2.0-33746258589 | en_US |
dc.identifier.hkuros | 97995 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-33746258589&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.spage | 17 | en_US |
dc.identifier.epage | 24 | en_US |
dc.publisher.place | Berlin | en_US |
dc.identifier.scopusauthorid | Ching, WK=13310265500 | en_US |
dc.identifier.scopusauthorid | Fung, ES=7005440799 | en_US |
dc.identifier.scopusauthorid | Ng, MK=34571761900 | en_US |
dc.identifier.issnl | 0302-9743 | - |