%0 Conference Paper %1 Wang2004a %A Wang, Tie %A Touchman, J.W. %A Xue, Guoliang %D 2004 %J Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE %K (artificial Bayesian Carlo K2 Monte algorithm, annealing belief biology computing, genetic genetics, global inference inference, intelligence), learning learning, local mechanisms, method, methods, model, network networks, optimized optimizer, probability probability, problem, problems, search simulated strengthened structure two-level %P 647-648 %R 10.1109/CSB.2004.1332531 %T Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks %X 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.

@inproceedings{Wang2004a, 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.}, added-at = {2009-09-12T19:19:34.000+0200}, author = {Wang, Tie and Touchman, J.W. and Xue, Guoliang}, biburl = {https://www.bibsonomy.org/bibtex/254bebce81140e4497f11c8883d47ab2e/mozaher}, doi = {10.1109/CSB.2004.1332531}, file = {:Wang2004a.pdf:PDF}, interhash = {aba6125557db11555c5fde65493d710c}, intrahash = {54bebce81140e4497f11c8883d47ab2e}, journal = {Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE}, keywords = {(artificial Bayesian Carlo K2 Monte algorithm, annealing belief biology computing, genetic genetics, global inference inference, intelligence), learning learning, local mechanisms, method, methods, model, network networks, optimized optimizer, probability probability, problem, problems, search simulated strengthened structure two-level}, month = {Aug.}, owner = {Mozaherul Hoque}, pages = { 647-648}, timestamp = {2009-09-12T19:19:43.000+0200}, title = {Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks}, year = 2004 }