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

Tie Wang; Jeffrey W. Touchman; Guoliang Xue

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

Tie Wang, Jeffrey W. Touchman, Guoliang Xue

IAFSE-SCAI: Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 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 language	English (US)
Title of host publication	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004
Pages	647-648
Number of pages	2
State	Published - Dec 1 2004
Event	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 - Stanford, CA, United States Duration: Aug 16 2004 → Aug 19 2004

Publication series

Name	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

Other

Other	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004
Country/Territory	United States
City	Stanford, CA
Period	8/16/04 → 8/19/04

ASJC Scopus subject areas

Engineering(all)

Cite this

Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks. / Wang, Tie; Touchman, Jeffrey W.; Xue, Guoliang.

Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004. 2004. p. 647-648 (Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, T, Touchman, JW & 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. Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004, pp. 647-648, Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004, Stanford, CA, United States, 8/16/04.

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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.",

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AB - 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.

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M3 - Conference contribution

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

Abstract

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ASJC Scopus subject areas

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