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Estimating Sparse Gene Regulatory Networks Using a Bayesian Linear Regression

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4 Author(s)
Sarder, P. ; Dept. of Electr. & Syst. Eng., Washington Univ., St. Louis, MO, USA ; Schierding, W. ; Cobb, J.P. ; Nehorai, Arye

In this paper, we propose a gene regulatory network (GRN) estimation method, which assumes that such networks are typically sparse, using time-series microarray datasets. We represent the regulatory relationships between the genes using weights, with the “net” regulation influence on a gene's expression being the summation of the independent regulatory inputs. We estimate the weights using a Bayesian linear regression method for sparse parameter vectors. We apply our proposed method to the extraction of differential gene expression software selected genes of a human buffy-coat microarray expression profile dataset of ventilator-associated pneumonia (VAP), and compare the estimation result with the GRNs estimated using both a correlation coefficient method and a database-based method ingenuity pathway analysis. A biological analysis of the resulting consensus network that is derived using the GRNs, estimated with both our and the correlation-coefficient methods results in four biologically meaningful subnetworks. Also, our method performs either better than or competitively with the existing well-established GRN estimation methods. Moreover, it performs comparatively with respect to: 1) the ground-truth GRNs for the in silico 50- and 100-gene datasets reported recently in the DREAM3 challenge and 2) the GRN estimated using a mutual information-based method for the top-ranked Bayesian analysis of time series (a Bayesian user-friendly software for analyzing time-series microarray experiments) selected genes of the VAP dataset.

Published in:

NanoBioscience, IEEE Transactions on  (Volume:9 ,  Issue: 2 )