Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

ABSTRACT: We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with

SUBMITTER: Wang YK 

PROVIDER: S-EPMC3743784 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature