Xiong Hao, Choe Yoonsuck
Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA.
Bioinformatics. 2008 Feb 15;24(4):553-60. doi: 10.1093/bioinformatics/btm623. Epub 2008 Jan 5.
Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit.
In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints.
The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.
从实验数据逆向工程遗传调控网络是遗传网络建模的第一步。线性状态空间模型，也称为线性动态模型，已被应用于从基因表达时间序列数据对遗传网络进行建模，但现有工作尚未考虑可用的结构信息。没有结构约束，估计的模型可能与生物学知识相矛盾，并且估计方法可能会过度拟合。
在本报告中，我们扩展了期望最大化（EM）算法，以纳入先验网络结构，并估计能够跟踪和预测基因表达谱的遗传调控网络。我们将我们的方法应用于合成数据和SOS数据，并表明我们的方法明显优于没有结构约束的常规EM。
可根据要求提供Matlab代码，SOS数据可从http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/下载，由Uri Alon提供。Zak的数据可从他的网站http://www.che.udel.edu/systems/people/zak获取。
