Parallel Bayesian network structure learning with application to gene networks

Olga Nikolova, Srinivas Aluru

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

9 Scopus citations

Abstract

Bayesian networks (BN) are probabilistic graphical models which are widely utilized in various research areas, including modeling complex biological interactions in the cell. Learning the structure of a BN is an NP-hard problem and exact solutions are limited to a few tens of variables. In this work, we present a parallel BN structure learning algorithm that combines principles of both heuristic and exact approaches and facilitates learning of larger networks. We demonstrate the applicability of our approach by an implementation on a Cray AMD cluster, and present experimental results for the problem of inferring gene networks. Our approach is work-optimal and achieves nearly perfect scaling.

Original languageEnglish (US)
Title of host publication2012 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 24th International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012 - Salt Lake City, UT, United States
Duration: Nov 10 2012Nov 16 2012

Publication series

NameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
ISSN (Print)2167-4329
ISSN (Electronic)2167-4337

Conference

Conference2012 24th International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012
CountryUnited States
CitySalt Lake City, UT
Period11/10/1211/16/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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