Integrative identification of core genetic regulatory modules via a structural model-based clustering method. Int J Comput Biol Drug Des 2011;4(2):127-46
Date
06/30/2011Pubmed ID
21712564DOI
10.1504/IJCBDD.2011.041007Scopus ID
2-s2.0-79959861120 (requires institutional sign-in at Scopus site) 2 CitationsAbstract
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.
Author List
Tang B, Chen SS, Jin VXAuthor
Victor X. Jin PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsAnimals
Cell Cycle
Cell Line, Tumor
Cluster Analysis
Computer Simulation
Gene Regulatory Networks
Genes, cdc
Genes, p53
Humans
Leukemia
Models, Genetic
Principal Component Analysis
Systems Biology