要点：

1、miRNA是一类重要的调控因子，miRNA调控网络的异常变化与多种疾病密切相关，通过观察miRNA靶基因及其下游基因网络的表达变化，可以反向推断出在疾病中（如恶性肿瘤）发生异常变化的miRNA调控网络；

2、与之前的模型相比，NPmiR（network propagation based miRNA inferences）方法考虑了miRNA的扰动对整个基因网络的影响，而且不仅仅是其直接作用靶基因，同时还考虑了不同条件下miRNA调控不同靶基因的问题；

3、NPmiR用的是前向模型，利用random-walk with restart模拟miRNA扰动的效果，然后观测该扰动与实际基因表达变化的相关性

论文链接：

http://www.biomedcentral.com/1471-2105/15/255/abstract

Ting Wang, Jin Gu#, Yanda Li. Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method. BMC Bioinformatics 2014, 15:255.

MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications   of the dys-regulated or perturbed miRNAs and their key target genes are important   for understanding the regulatory networks associated with the studied cellular processes.   Several computational methods have been developed to infer the perturbed miRNA regulatory   networks by integrating genome-wide gene expression data and sequence-based miRNA-target   predictions. However, most of them only use the expression information of the miRNA   direct targets, rarely considering the secondary effects of miRNA perturbation on   the global gene regulatory networks.

We proposed a network propagation based method to infer the perturbed miRNAs and their   key target genes by integrating gene expressions and global gene regulatory network   information. The method used random walk with restart in gene regulatory networks   to model the network effects of the miRNA perturbation. Then, it evaluated the significance   of the correlation between the network effects of the miRNA perturbation and the gene   differential expression levels with a forward searching strategy. Results show that   our method outperformed several compared methods in rediscovering the experimentally   perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset   of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory   networks of colorectal cancer, including several known oncogenic or tumor-suppressive   miRNAs, such as miR-17, miR-26 and miR-145.

Our network propagation based method takes advantage of the network effect of the   miRNA perturbation on its target genes. It is a useful approach to infer the perturbed   miRNAs and their key target genes associated with the studied biological processes   using gene expression data.