Integrative identification of core genetic regulatory modules via a structural model-based clustering method

Binhua Tang, Su Shing Chen, Victor X Jin

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)127-146
Number of pages20
JournalInternational Journal of Computational Biology and Drug Design
Volume4
Issue number2
DOIs
StatePublished - Jun 1 2011
Externally publishedYes

Fingerprint

Structural Models
Cluster Analysis
Life cycle
Dynamic models
Throughput
Systems Biology
Processing
Life Cycle Stages
Cell Cycle

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 journalArticle

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