Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network

Authors:
Seiya Imoto;Kim Sunyong;Takao Goto;Sachiyo Aburatani;Kousuke Tashiro;Satoru Kuhara;Satoru Miyano
Affiliations:
-;-;-;-;-;-;-
Venue:
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Year:
2002

Citing 5
Cited 8

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Introduction to Bayesian Networks

Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems

Probabilistic Networks and Expert Systems
Learning Bayesian networks: a unification for discrete and Gaussian domains

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data Using Differential Equations

DS '02 Proceedings of the 5th International Conference on Discovery Science
Dynamic Bayesian Network and Nonparametric Regression for Nonlinear Modeling of Gene Networks from Time Series Gene Expression Data

CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Revising Qualitative Models of Gene Regulation

DS '02 Proceedings of the 5th International Conference on Discovery Science
Inference, Modeling and Simulation of Gene Networks

CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Revising regulatory networks: from expression data to linear causal models

Journal of Biomedical Informatics
Mining Combinatorial Effects on Quantitative Traits from Protein Expression Data

Proceedings of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering
Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data

CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology

Quantified Score

Hi-index	0.02

Visualization

Abstract

We propose a new statistical method for constructing genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.