We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden state ...

Abstract: We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden.

Jun 5, 2006 · We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from ...

Abstract: We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden state ...

State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast.

Oct 7, 2009 · Our approach is based on a state space model that incorporates hidden regulators of gene expression. Kalman (K) smoothing and maximum (M) ...

State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast. We use linear ...

2006 Vol.1 No.1 ; 77-87, State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast

State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast · Rui YamaguchiT ...

State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast. We use linear ...