Adding hidden nodes to gene networks (extended abstract)

Benny Chor*, Tamir Tuller

*Corresponding author for this work

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

1 Scopus citations

Abstract

Bayesian networks are widely used for modelling gene networks. We investigate the problem of expanding a given Bayesian network by adding a hidden node - a node on which no experimental data are given. Finding a good expansion (a new hidden node and its neighborhood) can point to regions where the model is not rich enough, and help locate new, unknown variables that are important for understanding the network. We study the computational complexity of this expansion, show it is hard, and describe an EM based heuristic algorithm for solving it. The algorithm was applied to synthetic datasets and to yeast gene expression datasets, and produces good, encouraging results.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsInge Jonassen, Junhyong Kim
PublisherSpringer Verlag
Pages123-134
Number of pages12
ISBN (Print)3540230181, 9783540230182
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3240
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Bayesian networks
  • Compression
  • EM
  • Gene network
  • Maximum likelihood
  • Minimum description length
  • Network expansion

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