A1 Refereed original research article in a scientific journal
Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning
Authors: Larjo A, Lähdesmäki H
Publication year: 2015
Journal: EURASIP Journal on Bioinformatics and Systems Biology
Journal name in source: EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY
Journal acronym: EURASIP J BIOINFORM
Article number: ARTN 6
Issue: 1
Number of pages: 14
eISSN: 1687-4153
DOI: https://doi.org/10.1186/s13637-015-0024-7
Abstract
Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.
Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.
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