PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Biomarker discovery in microarray gene expression data with Gaussian processes
Wei Chu, Zoubin Ghahramani, F. Falciani and D.L. Wild
Bioinformatics Volume 21, pp. 3385-3393, 2005.

Abstract

In clinical practice, pathological phenotypes are often labelled with ordinal scales rather than binary, e.g. the Gleason grading system for tumor cell differentiation. However, in the literature of microarray analysis, these ordinal labels have been rarely treated in a principled way. This paper describes a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian inference framework.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1214
Deposited By:Wei Chu
Deposited On:27 November 2005