Wu Fang-Xiang, Liu Li-Zhi, Xia Zhang-Hang
Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, S7N 5A9, CANADA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:795-8. doi: 10.1109/IEMBS.2010.5626506.
Several methods have been proposed to infer gene regulatory networks from time course gene expression data. As the number of genes is much larger than the number of time points at which gene expression (mRNA concentration) is measured, most existing methods need some ad hoc assumptions to infer a unique gene regulatory network from time course gene expression data. It is well known that gene regulatory networks are sparse and stable. However, inferred network from most existing methods may not be stable. In this paper we propose a method to infer sparse and stable gene regulatory networks from time course gene expression data. Instead of ad hoc assumption, we formulate the inference of sparse and stable gene regulatory networks as constraint optimization problems, which can be easily solved. To investigate the performance of our proposed method, computational experiments are conducted on synthetic datasets.
已经提出了几种从时间序列基因表达数据推断基因调控网络的方法。由于基因数量远大于测量基因表达（mRNA浓度）的时间点数，大多数现有方法需要一些特设假设才能从时间序列基因表达数据推断出唯一的基因调控网络。众所周知，基因调控网络是稀疏且稳定的。然而，大多数现有方法推断出的网络可能不稳定。在本文中，我们提出了一种从时间序列基因表达数据推断稀疏且稳定的基因调控网络的方法。我们不是采用特设假设，而是将稀疏且稳定的基因调控网络的推断表述为约束优化问题，这些问题可以很容易地解决。为了研究所提出方法的性能，我们在合成数据集上进行了计算实验。
