@inproceedings{3a7ded98048246c59d52669226672a4f,
title = "Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network",
abstract = "We propose a new statistical method for constructing a 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.",
author = "S. Imoto and Kim Sunyong and T. Goto and S. Aburatani and K. Tashiro and S. Kuhara and S. Miyano",
year = "2002",
doi = "10.1109/CSB.2002.1039344",
language = "English",
series = "Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "219--227",
booktitle = "Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002",
address = "United States",
note = "1st International IEEE Computer Society Bioinformatics Conference, CSB 2002 ; Conference date: 14-08-2002 Through 16-08-2002",
}