Bayesian non-linear matching of pairwise microarray gene expressions

In this paper, we present a Bayesian non-linear model to analyze matching pairs of microarray expression data. This model generalizes, in terms of neural networks, standard linear matching models. As a practical application, we analyze data of patients with Acute Lymphoblastic Leukemia and we find out the best neural net model that relates the expression levels of two types of cytogenetically different samples from them.

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Year of publication:	2008-05
Authors:	Marín, Juan Miguel ; Nieto, Carmen
Institutions:	Departamento de Estadistica, Universidad Carlos III de Madrid
Subject:	MCMC computation \| Microarray data analysis \| Multidimensional scaling \| Neural network \| Spatial Bayesian methods

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Extent:	application/pdf
Series:	Statistics and Econometrics Working Papers.
Type of publication:	Book / Working Paper
Language:	English
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008480484