文章基本信息

标题：Inferring Causal Gene Targets from Time Course Expression Data
本地全文：下载
作者：Heeju Noh ; Hua Ziyi ; Rudiyanto Gunawan 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：26
页码：350-356
DOI：10.1016/j.ifacol.2016.12.151
语种：English
出版社：Elsevier
摘要：In this work, we proposed a network analysis method called DeltaNeTS for inferring genetic perturbations from temporal transcriptional expression profiles. While several efficacious and robust network analysis methods exist for steady state data, a direct application of these methods to analyze time series expression data often leads to a poor prediction performance. DeltaNeTS is an extension of our previous method DeltaNet, which involves a single-step inference of gene regulatory network and gene targets from transcriptional profiles. In order to prevent reversals in the causal directions of gene regulations when analyzing time series data, DeltaNeTS employs an additional constraint based on the time derivatives of the temporal expression profiles. We demonstrated the advantages of DeltaNeTS over DeltaNet and a network analysis method called Time Series Network Inference (TSNI), by analyzing time-series expression data from in silico simulations and from microarray assays of Saccharomyces cerevisiae (yeast) and cultured human airway epithelial cells.
关键词：time seriesgene expressionnetwork perturbationsmechanism of actionLasso