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) |
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| 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 |
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Other
| Other | Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 |
|---|---|
| Country | United States |
| City | Stanford, CA |
| Period | 8/16/04 → 8/19/04 |
ASJC Scopus subject areas
- Engineering(all)
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Cite this
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).