Abstract
We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.
Original language | English (US) |
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Pages (from-to) | 507-525 |
Number of pages | 19 |
Journal | Biostatistics |
Volume | 8 |
Issue number | 3 |
DOIs | |
State | Published - Jul 2007 |
Externally published | Yes |
Keywords
- Bayesian network
- Bioinformatics
- Dynamic linear model
- Mixture model
- Model-based clustering
- Prostate cancer
- Time-course gene expression
ASJC Scopus subject areas
- General Medicine