Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks

Tie Wang, Jeffrey W. Touchman, Guoliang Xue

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

19 Scopus citations

Abstract

Bayesian network is a common approach to study gene regulatory networks. Here, we explore the problem of inferring Bayesian structure from data that can be viewed as a search problem. The goal is to find a global optimized probability network model given the data. In this work, we propose a new search algorithm: Two-level Simulated Annealing (TLSA). TLSA performs simulated Annealing in two levels with strengthened local optimizer, and is less likely to get tracked at local optimizer. To illustrate the value of TLSA in Bayesian structure learning, the algorithms is applied on simulated datasets generated using the Monte Carlo method. The experimental results are compared with other learning algorithm such as K2.

Original languageEnglish (US)
Title of host publicationProceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004
Pages647-648
Number of pages2
StatePublished - Dec 1 2004
EventProceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 - Stanford, CA, United States
Duration: Aug 16 2004Aug 19 2004

Publication series

NameProceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

Other

OtherProceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004
CountryUnited States
CityStanford, CA
Period8/16/048/19/04

ASJC Scopus subject areas

  • Engineering(all)

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    Wang, T., Touchman, J. W., & Xue, G. (2004). Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks. In Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 (pp. 647-648). (Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004).