Title :
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
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;
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
DOI :
10.1109/GENSIPS.2006.353181
