Liu Yan, Niculescu-Mizil Alexandru, Lozano Aurélie, Lu Yong
Computer Science Department, University of Southern California, 941 Bloom Walk SAL 300, Los Angeles, CA 90089, USA.
J Bioinform Comput Biol. 2011 Apr;9(2):231-50. doi: 10.1142/s0219720011005525.
Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L(1) penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.
许多基因和生物过程在不同物种中以相似的方式发挥作用。跨物种基因表达分析作为一种表征细胞动态特性的强大工具，已得到了许多应用，例如识别细胞周期基因的保守核心集。然而，据我们所知，在开发适当技术以从跨物种的时间序列微阵列数据中捕捉基因之间的因果关系方面，所做的工作有限。在本文中，我们提出了带有L(1)惩罚的隐马尔可夫随机场回归，以揭示不同物种的调控网络结构。该算法通过隐藏成分图提供了一个跨物种共享信息的框架，并且能够轻松整合跨物种的领域知识。我们在两个合成数据集上演示了我们的方法，并将其应用于从先天免疫反应数据中发现因果图。
