Abstract
Regulatory modules play fundamental roles in processing and dispatching signals in cell life cycle. Although current clustering methods may reduce data complexity to lower dimension, they tend to neglect biological meanings within high-throughput data. We propose a module-detection algorithm through defining network activity measures and associating them through a weighted clustering approach. We verify our method on diverse models and it provides a unique perspective for analysing model dynamics and expression data, especially with consideration of inherent biological meanings. As it can detect core regulatory modules effectively, it facilitates pathway/network modelling in systems biology.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 127-146 |
| Number of pages | 20 |
| Journal | International Journal of Computational Biology and Drug Design |
| Volume | 4 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2011 |
| Externally published | Yes |
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Keywords
- Dynamic models
- Genetic regulatory module
- Structural model clustering
ASJC Scopus subject areas
- Computer Science Applications
- Drug Discovery
Cite this
Integrative identification of core genetic regulatory modules via a structural model-based clustering method. / Tang, Binhua; Chen, Su Shing; Jin, Victor X.
In: International Journal of Computational Biology and Drug Design, Vol. 4, No. 2, 01.06.2011, p. 127-146.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Integrative identification of core genetic regulatory modules via a structural model-based clustering method
AU - Tang, Binhua
AU - Chen, Su Shing
AU - Jin, Victor X
PY - 2011/6/1
Y1 - 2011/6/1
N2 - Regulatory modules play fundamental roles in processing and dispatching signals in cell life cycle. Although current clustering methods may reduce data complexity to lower dimension, they tend to neglect biological meanings within high-throughput data. We propose a module-detection algorithm through defining network activity measures and associating them through a weighted clustering approach. We verify our method on diverse models and it provides a unique perspective for analysing model dynamics and expression data, especially with consideration of inherent biological meanings. As it can detect core regulatory modules effectively, it facilitates pathway/network modelling in systems biology.
AB - Regulatory modules play fundamental roles in processing and dispatching signals in cell life cycle. Although current clustering methods may reduce data complexity to lower dimension, they tend to neglect biological meanings within high-throughput data. We propose a module-detection algorithm through defining network activity measures and associating them through a weighted clustering approach. We verify our method on diverse models and it provides a unique perspective for analysing model dynamics and expression data, especially with consideration of inherent biological meanings. As it can detect core regulatory modules effectively, it facilitates pathway/network modelling in systems biology.
KW - Dynamic models
KW - Genetic regulatory module
KW - Structural model clustering
UR - http://www.scopus.com/inward/record.url?scp=79959861120&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959861120&partnerID=8YFLogxK
U2 - 10.1504/IJCBDD.2011.041007
DO - 10.1504/IJCBDD.2011.041007
M3 - Article
C2 - 21712564
AN - SCOPUS:79959861120
VL - 4
SP - 127
EP - 146
JO - International Journal of Computational Biology and Drug Design
JF - International Journal of Computational Biology and Drug Design
SN - 1756-0756
IS - 2
ER -