Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

• Published in: Computer methods and programs in biomedicine Vol. 91; no. 2; pp. 110 - 121
• Main Authors: Armañanzas, Rubén, Inza, Iñaki, Larrañaga, Pedro
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.08.2008
• Subjects: Algorithms; Bayes Theorem; Bayesian network classifiers; Computer Simulation; DNA microarrays; Gene Expression Profiling - methods; Gene interactions; Genes - physiology; Internal Medicine; Knowledge discovery; Models, Biological; Oligonucleotide Array Sequence Analysis - methods; Other; Pattern Recognition, Automated - methods; Proteome - metabolism; Robust arc identification; Signal Transduction - physiology; Knowledge discovery; DNA microarrays; Robust arc identification; Gene interactions; Bayesian network classifiers
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2008.02.010

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies.