Uncovering gene regulatory networks using variational Bayes variable selection

Author

Luna, I.T. ; Yufang Yin ; Yufei Huang ; Padillo, D.P.R. ; Perez, M.C.C.

Author_Institution

Dept. of Appl. Phys., Granada Univ., Granada

fYear

2006

fDate

28-30 May 2006

Firstpage

111

Lastpage

112

Abstract

In this paper, we propose a Bayesian approach for reconstructing gene regulatory networks (GRNs) based on microarray data. We focus on a variable selection formulation and develop a solution by a variational Bayes expectation maximization (VBEM) learning rule. The major advantage of the VBEM solution over Monte Carlo sampling based approach is its lower computational complexity. This makes it appealing for uncovering large networks.

Keywords

belief networks; biology computing; expectation-maximisation algorithm; genetics; learning (artificial intelligence); gene regulatory network; learning rule; microarray data; variational Bayes expectation maximization; variational Bayes variable selection; Bayesian methods; Gaussian noise; Inference algorithms; Input variables; Iterative algorithms; Markov processes; Monte Carlo methods; Physics; Sampling methods; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Conference_Location

College Station, TX

Print_ISBN

1-4244-0384-7

Electronic_ISBN

1-4244-0385-5

Type

conf

DOI

10.1109/GENSIPS.2006.353181

Filename

4161802

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2442903