CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011 vol.8 Issue No.05 - September/October

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Issue No.05 - September/October (2011 vol.8)

pp: 1385-1392

Marco Muselli , Consiglio Nazionale delle Ricerche, Genova

Alberto Bertoni , Università degli Studi di Milano, Milano

Marco Frasca , Università degli Studi di Milano, Milano

Alessandro Beghini , Università degli Studi di Milano, Milano

Francesca Ruffino , Università degli Studi di Milano, Milano

Giorgio Valentini , Università degli Studi di Milano, Milano

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.83

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.

INDEX TERMS

Gene selection, feature selection, mathematical models, gene expression, Boolean functions.

CITATION

Marco Muselli, Alberto Bertoni, Marco Frasca, Alessandro Beghini, Francesca Ruffino, Giorgio Valentini, "A Mathematical Model for the Validation of Gene Selection Methods",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.8, no. 5, pp. 1385-1392, September/October 2011, doi:10.1109/TCBB.2010.83REFERENCES