TY  - JOUR
A1  - Omranian, Nooshin
A1  - Eloundou-Mbebi, Jeanne Marie Onana
A1  - Müller-Röber, Bernd
A1  - Nikoloski, Zoran
T1  - Gene regulatory network inference using fused LASSO on multiple data sets
JF  - Scientific reports
N2  - Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.
Y1  - 2016
U6  - https://doi.org/10.1038/srep20533
SN  - 2045-2322
VL  - 6
PB  - Nature Publ. Group
CY  - London
ER  -