Parallel discovery of direct causal relations and Markov boundaries with applications to gene networks

Olga Nikolova, Srinivas Aluru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Bayesian networks enable formal probabilistic reasoning on a set of interacting variables of a domain, and have been shown to have broad applicability. More specifically, in bioinformatics Bayesian networks are used to model gene interactions. Learning the structure of a Bayesian network is an NP-hard problem making it necessary to employ heuristics for solving large-scale problems. In this paper, we present parallel algorithms for two problems that arise in relation with network structure learning and analysis: (i) the discovery of all direct causal relations for each variable, i.e., the set of parents and children of each node in the corresponding Bayesian network, and (ii) the computation of Markov boundary of each variable, defined as the minimal set of variables that shield the target variable from all other variables in the domain. Our parallel algorithms are based on state-of-the art constraint-based heuristic optimization methods. They are shown to be work-optimal and communication efficient, and exhibit nearly perfect scaling.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 International Conference on Parallel Processing, ICPP 2011
Pages512-521
Number of pages10
DOIs
StatePublished - 2011
Externally publishedYes
Event40th International Conference on Parallel Processing, ICPP 2011 - Taipei City, Taiwan, Province of China
Duration: Sep 13 2011Sep 16 2011

Publication series

NameProceedings of the International Conference on Parallel Processing
ISSN (Print)0190-3918

Conference

Conference40th International Conference on Parallel Processing, ICPP 2011
CountryTaiwan, Province of China
CityTaipei City
Period9/13/119/16/11

Keywords

  • Bayesian networks
  • Causal relations
  • Constraint-based learning
  • Markov boundaries

ASJC Scopus subject areas

  • Software
  • Mathematics(all)
  • Hardware and Architecture

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