Luo Weijun, Woolf Peter J
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Methods Mol Biol. 2010;674:401-18. doi: 10.1007/978-1-60761-854-6_23.
Probabilistic methods such as mutual information and Bayesian networks have become a major category of tools for the reconstruction of regulatory relationships from quantitative biological data. In this chapter, we describe the theoretic framework and the implementation for learning gene regulatory networks using high-order mutual information via the MI3 method (Luo et al. (2008) BMC Bioinformatics 9, 467; Luo (2008) Gene regulatory network reconstruction and pathway inference from high throughput gene expression data. PhD thesis). We also cover the closely related Bayesian network method in detail.
诸如互信息和贝叶斯网络等概率方法已成为从定量生物学数据重建调控关系的主要工具类别。在本章中，我们描述了使用MI3方法通过高阶互信息学习基因调控网络的理论框架和实现（Luo等人，（2008年）《BMC生物信息学》9卷，467页；Luo（2008年）《从高通量基因表达数据重建基因调控网络和推断通路》。博士论文）。我们还详细介绍了密切相关的贝叶斯网络方法。
