Learning gene regulatory networks via globally regularized risk minimization

Authors:
Yuhong Guo;Dale Schuurmans
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Canada;Department of Computing Science, University of Alberta, Edmonton, Canada
Venue:
RECOMB-CG'07 Proceedings of the 2007 international conference on Comparative genomics
Year:
2007

Citing 9
Cited 0

Linear Modeling of Genetic Networks from Experimental Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Grafting: fast, incremental feature selection by gradient descent in function space

The Journal of Machine Learning Research
Convex Optimization

Convex Optimization
Feature selection, L1 vs. L2 regularization, and rotational invariance

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Module Networks

The Journal of Machine Learning Research
Advances to Bayesian network inference for generating causal networks from observational biological data

Bioinformatics
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data

Bioinformatics
A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae

Bioinformatics
An effective structure learning method for constructing gene networks

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Learning the structure of a gene regulatory network from timeseries gene expression data is a significant challenge. Most approaches proposed in the literature to date attempt to predict the regulators of each target gene individually, but fail to share regulatory information between related genes. In this paper, we propose a new globally regularized risk minimization approach to address this problem. Our approach first clusters genes according to their time-series expression profiles--identifying related groups of genes. Given a clustering, we then develop a simple technique that exploits the assumption that genes with similar expression patterns are likely to be co-regulated by encouraging the genes in the same group to share common regulators. Our experiments on both synthetic and real gene expression data suggest that our new approach ismore effective at identifying important transcription factor based regulatory mechanisms than the standard independent approach and a prototype based approach.