PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Biological specifications for a synthetic gene expression data generation model
Francesca Ruffino, Marco Muselli and Giorgio Valentini
In: Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes on Computer Science (3849). (2005) Springer .

Abstract

An open problem in gene expression data analysis is the evaluation of the performance of gene selection methods applied to discover biologically relevant sets of genes. The problem is difficult, as the entire set of genes involved in specific biological processes is usually unknown or only partially known, making unfeasible a correct comparison between different gene selection methods. The natural solution to this problem consists in developing an artificial model to generate gene expression data, in order to know in advance the set of biologically relevant genes. The models proposed in the literature, even if useful for a preliminary evaluation of gene selection methods, did not explicitly consider the biological characteristics of gene expression data. The main aim of this work is to individuate the main biological characteristics that need to be considered to design a model for validating gene selection methods based on the analysis of DNA microarray data.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2071
Deposited By:Giorgio Valentini
Deposited On:30 January 2006