A dynamic time order network for time-series gene expression data analysis.

Pengyue Zhang, Raphaël Mourad, Yang Xiang, Kun Huang, Tim Huang, Kenneth Nephew, Yunlong Liu, Lang Li

Research output: Contribution to journalArticle

35 Scopus citations


Typical analysis of time-series gene expression data such as clustering or graphical models cannot distinguish between early and later drug responsive gene targets in cancer cells. However, these genes would represent good candidate biomarkers. We propose a new model - the dynamic time order network - to distinguish and connect early and later drug responsive gene targets. This network is constructed based on an integrated differential equation. Spline regression is applied for an accurate modeling of the time variation of gene expressions. Then a likelihood ratio test is implemented to infer the time order of any gene expression pair. One application of the model is the discovery of estrogen response biomarkers. For this purpose, we focused on genes whose responses are late when the breast cancer cells are treated with estradiol (E2). Our approach has been validated by successfully finding time order relations between genes of the cell cycle system. More notably, we found late response genes potentially interesting as biomarkers of E2 treatment.

Original languageEnglish (US)
JournalBMC Systems Biology
Volume6 Suppl 3
StatePublished - 2012
Externally publishedYes


ASJC Scopus subject areas

  • Molecular Biology
  • Structural Biology
  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

Cite this

Zhang, P., Mourad, R., Xiang, Y., Huang, K., Huang, T., Nephew, K., Liu, Y., & Li, L. (2012). A dynamic time order network for time-series gene expression data analysis. BMC Systems Biology, 6 Suppl 3. https://doi.org/10.1186/1752-0509-6-S1-S9