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 2011 |
Externally published | Yes |
Keywords
- Dynamic models
- Genetic regulatory module
- Structural model clustering
ASJC Scopus subject areas
- Drug Discovery
- Computer Science Applications