Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Analysis of variance components in gene expression data
Bioinformatics
Evaluating Switching Neural Networks for Gene Selection
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Evaluating switching neural networks through artificial and real gene expression data
Artificial Intelligence in Medicine
A Multivariate Algorithm for Gene Selection Based on the Nearest Neighbor Probability
Computational Intelligence Methods for Bioinformatics and Biostatistics
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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.