Gene expression modeling through positive Boolean functions

作     者：Ruffino, Francesca Muselli, Marco Valentini, Giorgio 

作者机构：Univ Milan Dipartimento Sci Informaz DSI Milan Italy CNR IEIIT Genoa Italy 

出 版 物：《INTERNATIONAL JOURNAL OF APPROXIMATE REASONING》 (国际近似理论杂志)

年 卷 期：2008年第47卷第1期

页      面：97-108页

核心收录：

学科分类：07[理学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：Ministero dell’Istruzione  dell’Università e della Ricerca  MIUR 

主　　题：gene expression modeling gene selection gene expression data clustering positive Boolean functions DNA microarrays 

摘      要：In the framework of gene expression data analysis, the selection of biologically relevant sets of genes and the discovery of new subclasses of diseases at bio-molecular level represent two significant problems. Unfortunately, in both cases the correct solution is usually unknown and the evaluation of the performance of gene selection and clustering methods is difficult and in many cases unfeasible. A natural approach to this complex issue consists in developing an artificial model for the generation of biologically plausible gene expression data, thus allowing to know in advance the set of relevant genes and the functional classes involved in the problem. In this work we propose a mathematical model, based on positive Boolean functions, for the generation of synthetic gene expression data. Despite its simplicity, this model is sufficiently rich to take account of the specific peculiarities of gene expression, including the biological variability, viewed as a sort of random source. As an applicative example, we also provide some data simulations and numerical experiments for the analysis of the performances of gene selection methods. (c) 2007 Elsevier Inc. All rights reserved.