Learning and validating Bayesian network models of gene networks

Jose M. Peña*, Johan Björkegren, Jesper Tegner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

We propose a framework for learning from data and validating Bayesian network models of gene networks. The learning phase selects multiple locally optimal models of the data and reports the best of them. The validation phase assesses the confidence in the model reported by studying the different locally optimal models obtained in the learning phase. We prove that our framework is asymptotically correct under the faithfulness assumption. Experiments with real data (320 samples of the expression levels of 32 genes involved in Saccharomyces cerevisiae, i.e. baker's yeast, pheromone response) show that our framework is reliable.

Original languageEnglish (US)
Title of host publicationAdvances in Probabilistic Graphical Models
EditorsPeter Lucas, Jose Gamez, Antionio Salmero
Pages359-375
Number of pages17
DOIs
StatePublished - Jun 22 2007

Publication series

NameStudies in Fuzziness and Soft Computing
Volume213
ISSN (Print)1434-9922

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics

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