Latent Structure Models for the Analysis of Gene Expression Data

Cluster methods have been successfully applied in gene expression data analysis to address tumor classification. By grouping tissue samples into homogeneous subsets, more systematic characterization can be developed and new subtypes of tumors be discovered. Central to cluster analysis is the notion of similarity between the individual samples. In this paper, we propose latent structure models as a framework where dependence among genes and thus relationship between samples can be modelled in a better way in terms of topology and flexibility. A latent structure model is a Bayesian network where the network structure contains at least a rooted tree including all variables, only variables at the leaf nodes are observed, and the structure after deleting all the observed variables is a rooted tree. The main gain in using latent structure models is that they provide a principled and systematic method to handle the dependence among genes. There are other benefits offered by latent structu...

• Type: Conference
• Year: 2003
• Where: CSB
• Authors: Dong Hua, Dechang Chen, Xiuzhen Cheng, Abdou Youssef
• Keywords: CSB 2003, Latent Structure Models, Latent Structures Model, Rooted Tree