PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A mathematical model for the validation of gene selection methods
Marco Muselli, Alberto Bertoni, Marco Frasca, Alessandro Beghini, Francesca Ruffino and Giorgio Valentini
IEEE ACM Transactions on Computational Biology and Bioinformatics Volume 8, Number 5, pp. 1385-1392, 2011.

This is the latest version of this eprint.

Abstract

Gene selection methods aim at determining biologically relevant subsets of genes in DNA microarray experiments. However, their assessment and validation represent a major difficulty since the subset of biologically relevant genes is usually unknown. To solve this problem a novel procedure for generating biologically plausible synthetic gene expression data is proposed. It is based on a proper mathematical model representing gene expression signatures and expression profiles through Boolean threshold functions. The results show that the proposed procedure can be successfully adopted to analyze the quality of statistical and machine learning-based gene selection algorithms.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:9138
Deposited By:Giorgio Valentini
Deposited On:21 February 2012

Available Versions of this Item