title: A mathematical model for the validation of gene selection methods.
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.
authors.affiliation: Consiglio Nazionale delle Ricerche, Genova. marco.muselli@ieiit.cnr.it
authors.forename: Marco
authors.initials: M
authors.lastname: Muselli
authors.forename: Alberto
authors.initials: A
authors.lastname: Bertoni
authors.forename: Marco
authors.initials: M
authors.lastname: Frasca
authors.forename: Alessandro
authors.initials: A
authors.lastname: Beghini
authors.forename: Francesca
authors.initials: F
authors.lastname: Ruffino
authors.forename: Giorgio
authors.initials: G
authors.lastname: Valentini
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000592:standards*; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results