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

@article{Imoto2002BayesianNA,
  title={Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network},
  author={Seiya Imoto and SunYong Kim and Takao Goto and Sachiyo Aburatani and Kousuke Tashiro and Satoru Kuhara and Satoru Miyano},
  journal={Proceedings. IEEE Computer Society Bioinformatics Conference},
  year={2002},
  volume={1},
  pages={219-27}
}
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 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. Selecting the optimal graph, which gives the best… CONTINUE READING
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