File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/ISB.2012.6314142
- Scopus: eid_2-s2.0-84868641202
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Application of Granger causality to gene regulatory network discovery
Title | Application of Granger causality to gene regulatory network discovery |
---|---|
Authors | |
Keywords | DNA microarray Gene regulatory network Granger causality Model validation Pairwise Spurious discovery |
Issue Date | 2012 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515 |
Citation | The 6th IEEE International Conference on Systems Biology (ISB 2012), Xi'an, China, 18-20 August 2012. In Conference Proceedings, 2012, p. 232-239 How to Cite? |
Abstract | Granger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions. © 2012 IEEE. |
Description | Article no. 6314142 |
Persistent Identifier | http://hdl.handle.net/10722/186739 |
ISBN |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tam, GHF | en_US |
dc.contributor.author | Chang, C | en_US |
dc.contributor.author | Hung, YS | en_US |
dc.date.accessioned | 2013-08-20T12:19:20Z | - |
dc.date.available | 2013-08-20T12:19:20Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | The 6th IEEE International Conference on Systems Biology (ISB 2012), Xi'an, China, 18-20 August 2012. In Conference Proceedings, 2012, p. 232-239 | en_US |
dc.identifier.isbn | 978-1-4673-4398-5 | - |
dc.identifier.uri | http://hdl.handle.net/10722/186739 | - |
dc.description | Article no. 6314142 | - |
dc.description.abstract | Granger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions. © 2012 IEEE. | - |
dc.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515 | - |
dc.relation.ispartof | IEEE International Conference on Systems Biology Proceedings | en_US |
dc.subject | DNA microarray | - |
dc.subject | Gene regulatory network | - |
dc.subject | Granger causality | - |
dc.subject | Model validation | - |
dc.subject | Pairwise | - |
dc.subject | Spurious discovery | - |
dc.title | Application of Granger causality to gene regulatory network discovery | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Tam, GHF: hftam@eee.hku.hk | en_US |
dc.identifier.email | Chang, C: cqchang@eee.hku.hk | en_US |
dc.identifier.email | Hung, YS: yshung@eee.hku.hk | - |
dc.identifier.authority | Chang, C=rp00095 | en_US |
dc.identifier.authority | Hung, YS=rp00220 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ISB.2012.6314142 | - |
dc.identifier.scopus | eid_2-s2.0-84868641202 | - |
dc.identifier.hkuros | 220041 | en_US |
dc.identifier.spage | 232 | - |
dc.identifier.epage | 239 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 140103 | - |