A general co-expression network-based approach to gene expression analysis: comparison and applications-Reference-Cited by-同舟云学术

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A general co-expression network-based approach to gene expression analysis: comparison and applications

Published:2010-02-02 Issue:1 Volume:4 Page:
ISSN:1752-0509
Container-title:BMC Systems Biology
language:en
Short-container-title:BMC Syst Biol

Author:

Ruan Jianhua,Dean Angela K,Zhang Weixiong

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Modelling and Simulation,Structural Biology

Link

http://link.springer.com/content/pdf/10.1186/1752-0509-4-8.pdf

Reference59 articles.

1. Eisen M, Spellman P, Brown P, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95: 14863-8. 10.1073/pnas.95.25.14863

2. Tegner J, Yeung M, Hasty J, Collins J: Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA. 2003, 100: 5944-9. 10.1073/pnas.0933416100

3. Friedman N, Linial M, Nachman I, Peer D: Using Bayesian networks to analyze expression data. J Comput Biol. 2000, 7: 601-20. 10.1089/106652700750050961

4. Davidich M, Bornholdt S: Boolean network model predicts cell cycle sequence of fission yeast. PLoS ONE. 2008, 3: e1672- 10.1371/journal.pone.0001672

5. Presson A, Sobel E, Papp J, Suarez C, Whistler T, Rajeevan M, Vernon S, Horvath S: Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome. BMC Syst Biol. 2008, 2: 95- 10.1186/1752-0509-2-95

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