PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Modeling gene expression data via positive Boolean functions
F. Ruffino, M. Muselli and G. Valentini
In: NETTAB 2006 workshop on Distributed Applications, Web Services, Tools and GRID Infrastructures for Bioinformatics, S. Margherita di Pula (CA), Italy(2006).

Abstract

In this work we propose an artificial model for the generation of biologically plausible gene expression data to be used in the evaluation of the performance of gene selection and clustering methods. The model, allows to fix in advance the set of relevant genes and the functional classes involved in the problem; the input-output relationship is constructed by synthesizing a positive Boolean function. Despite its simplicity, it is sufficiently rich to take account of the specific peculiarities of gene expression, including the biological variability. As an applicative example, we also provide some data simulations and numerical experiments for the analysis of the performances of gene selection methods.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2365
Deposited By:Giorgio Valentini
Deposited On:22 November 2006