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DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways.

Abstract：

:Although a number of methods have been proposed for identifying differentially expressed pathways (DEPs), few efforts consider the dynamic components of pathway networks, i.e., gene links. We here propose a signaling dynamics detection method for identification of DEPs, DynSig, which detects the molecular signaling changes in cancerous cells along pathway topology. Specifically, DynSig relies on gene links, instead of gene nodes, in pathways, and models the dynamic behavior of pathways based on Markov chain model (MCM). By incorporating the dynamics of molecular signaling, DynSig allows for an in-depth characterization of pathway activity. To identify DEPs, a novel statistic of activity alteration of pathways was formulated as an overall signaling perturbation score between sample classes. Experimental results on both simulation and real-world datasets demonstrate the effectiveness and efficiency of the proposed method in identifying differential pathways.

journal_name

Genes (Basel)

journal_title

Genes

authors

Shi M,Chong Y,Shen W,Xie XP,Wang HQ

doi

10.3390/genes9070323

subject

Has Abstract

pub_date

2018-06-27 00:00:00

issue

7

issn

2073-4425

pii

genes9070323

journal_volume

9

pub_type

杂志文章

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