Inference of gene-regulatory networks using message-passing algorithms

Author

Shamaiah, Manohar ; Lee, Sang Hyun ; Vikalo, Haris

Author_Institution

Dept. of ECE, Univ. of Texas at Austin, Austin, TX, USA

fYear

2010

fDate

10-12 Nov. 2010

Firstpage

1

Lastpage

4

Abstract

We present an application of message-passing techniques to gene regulatory network inference. The network inference is posed as a constrained linear regression problem, and solved by a distributed computationally efficient message-passing algorithm. Performance of the proposed algorithm is tested on gold standard data sets and evaluated using metrics provided by the DREAM2 challenge. Performance of the proposed algorithm is comparable to that of the techniques which yielded the best results in the DREAM2 challenge competition.

Keywords

biology computing; complex networks; genetics; message passing; molecular biophysics; regression analysis; DREAM2 challenge; computationally efficient message passing algorithm; constrained linear regression problem; distributed message passing algorithm; gene regulatory network; network inference; Data models; Gene expression; Graphical models; Inference algorithms; Mathematical model; Message passing; Optimization; Gene regulatory networks; L1-regularized model; Message passing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics (GENSIPS), 2010 IEEE International Workshop on

Conference_Location

Cold Spring Harbor, NY

ISSN

2150-3001

Print_ISBN

978-1-61284-791-7

Type

conf

DOI

10.1109/GENSIPS.2010.5719683

Filename

5719683