Machine Learning - Special issue on learning with probabilistic representations
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Journal of Biomedical Informatics - Special issue: Clinical machine learning
New probabilistic graphical models for genetic regulatory networks studies
Journal of Biomedical Informatics
Pattern classification in DNA microarray data of multiple tumor types
Pattern Recognition
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
The Journal of Machine Learning Research
A Blocking Strategy to Improve Gene Selection for Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A review of feature selection techniques in bioinformatics
Bioinformatics
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Ensemble transcript interaction networks: A case study on Alzheimer's disease
Computer Methods and Programs in Biomedicine
A systematic approach to embedded biomedical decision making
Computer Methods and Programs in Biomedicine
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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.