Sciweavers

Share
AINA
2008
IEEE

Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling

11 years 21 days ago
Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling
The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostatic dynamics of cell cycle systems to the response of key genes to the onset of cancer or infectious disease. However, microarray data frequently contain a significant number of missing values making the application of common multivariate analysis methods, all of which require complete expression matrices, difficult. In order to preserve the experimentally expensive non-missing data points in time series gene expression data, methods are needed to estimate the missing values in such a way that preserves the latent interdependencies among time points within individual expression profiles. Thus we propose modeling gene expression profiles as simple linear and Gaussian dynamical systems and apply the Kalman filter to estimate missing values. While other current advanced estimation methods are either sensitiv...
Connie Phong, Raul Singh
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where AINA
Authors Connie Phong, Raul Singh
Comments (0)
books