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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology

Open Access Research

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

Pengyue Zhang1, Raphaël Mourad1*, Yang Xiang2, Kun Huang2, Tim Huang3, Kenneth Nephew4, Yunlong Liu1 and Lang Li1

Author Affiliations

1 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA

2 Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA

3 Laboratory of Breast Cancer Epigenomics, The Ohio State University, Columbus, OH 43210, USA

4 Laboratory of Ovarian Cancer Epigenomics, Indiana University, Bloomington, IN 47405, USA

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BMC Systems Biology 2012, 6(Suppl 3):S9  doi:10.1186/1752-0509-6-S3-S9

Published: 17 December 2012

Abstract

Background

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.

Results

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).

Conclusions

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.