Use of microarray data via model-based classification in the study and prediction of survival from lung cancer

Liat Ben-Tovin Jones, Shu-Kay Ng, Christophe Ambroiose, Katrina Monico, Nazim Khan, Geoff McLachlan

Research output: Chapter in Book/Conference paperChapter

Abstract

We applied a model-based clustering approach to classify tumor tissues on the basis of microarray gene expression. The impact of this classification on cancer biology and clinical outcome was studied. In particular, the association between the clusters so formed and patient survival (recurrence) times was examined. The approach was illustrated using the four CAMDA’03 lung cancer datasets. We showed that the gene expression-based clustering is a powerful predictor of the outcome of disease, in addition to current systems based on histopathology criteria and extent of disease at presentation.
Original languageEnglish
Title of host publication Methods of Microarray Data Analysis IV
EditorsJennifer Shoemaker, Simon Lin
PublisherSpringer NY
Chapter12
Pages163-173
Number of pages11
Publication statusPublished - 2005

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Jones, L. B-T., Ng, S-K., Ambroiose, C., Monico, K., Khan, N., & McLachlan, G. (2005). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. In J. Shoemaker, & S. Lin (Eds.), Methods of Microarray Data Analysis IV (pp. 163-173). Springer NY.