Liu Li-Zhi, Wu Fang-Xiang, Zhang Wen-Jun
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
IET Syst Biol. 2015 Feb;9(1):16-24. doi: 10.1049/iet-syb.2013.0060.
Genes regulate each other and form a gene regulatory network (GRN) to realise biological functions. Elucidating GRN from experimental data remains a challenging problem in systems biology. Numerous techniques have been developed and sparse linear regression methods become a promising approach to infer accurate GRNs. However, most linear methods are either based on steady-state gene expression data or their statistical properties are not analysed. Here, two sparse penalties, adaptive least absolute shrinkage and selection operator and smoothly clipped absolute deviation, are proposed to infer GRNs from time-course gene expression data based on an auto-regressive model and their Oracle properties are proved under mild conditions. The effectiveness of those methods is demonstrated by applications to in silico and real biological data.
基因相互调控并形成基因调控网络（GRN）以实现生物学功能。从实验数据中阐明基因调控网络在系统生物学中仍然是一个具有挑战性的问题。已经开发了许多技术，稀疏线性回归方法成为推断准确基因调控网络的一种有前途的方法。然而，大多数线性方法要么基于稳态基因表达数据，要么没有分析其统计特性。在此，提出了两种稀疏惩罚，即自适应最小绝对收缩和选择算子以及平滑截断绝对偏差，以基于自回归模型从时间序列基因表达数据中推断基因调控网络，并在温和条件下证明了它们的神谕性质。通过应用于计算机模拟和真实生物学数据证明了这些方法的有效性。
