Estimating sparse gene regulatory networks using a bayesian linear regression

IEEE Transactions on Nanobioscience
Pinaki SarderArye Nehorai

Abstract

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...Continue Reading

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Citations

Apr 14, 2011·Theory in Biosciences = Theorie in Den Biowissenschaften·Markus KirkilionisLuca Sbano
Sep 12, 2015·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Shing-Chow ChanKai-Man Tsui

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