We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we pr...
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...
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 con...
Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Abur...