Linking gene-expression experiments with survival-time data.
Author(s)
Jones, Liat Ben-Tovim
Ng, Shu-Kay
Monico, Katrina
McLachlan, Geoff
Griffith University Author(s)
Year published
2004
Metadata
Show full item recordAbstract
We apply a model-based clustering approach to classify tumour tissues on the basis of microarray gene expression. The association between the clusters so formed and patient survival (recurrence) times is examined. The approach is illustrated using the lung cancer data set of Wigle et al. (2002). We show that the prognosis clustering is a powerful predictor of the outcome of disease, in addition to the stage of disease at presentation.We apply a model-based clustering approach to classify tumour tissues on the basis of microarray gene expression. The association between the clusters so formed and patient survival (recurrence) times is examined. The approach is illustrated using the lung cancer data set of Wigle et al. (2002). We show that the prognosis clustering is a powerful predictor of the outcome of disease, in addition to the stage of disease at presentation.
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Conference Title
Proceedings of the 19th International Workshop on Statistical Modelling