A Bayes random field approach for integrative large-scale regulatory network analysis

Yuan, Yinyin and Li, Chang-Tsun 2008, A Bayes random field approach for integrative large-scale regulatory network analysis, Journal of integrative bioinformatics, vol. 5, no. 2, doi: 10.1515/jib-2008-99.

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Title A Bayes random field approach for integrative large-scale regulatory network analysis
Author(s) Yuan, Yinyin
Li, Chang-TsunORCID iD for Li, Chang-Tsun orcid.org/0000-0003-4735-6138
Journal name Journal of integrative bioinformatics
Volume number 5
Issue number 2
Total pages 20
Publisher De Gruyter Publishing
Place of publication Berlin, Germany
Publication date 2008
ISSN 1613-4516
Summary We present a Bayes-Random Fields framework which is capable of integrating unlimited data sources for discovering relevant network architecture of large-scale networks. The random field potential function is designed to impose a cluster constraint, teamed with a full Bayesian approach for incorporating heterogenous data sets. The probabilistic nature of our framework facilitates robust analysis in order to minimize the influence of noise inherent in the data on the inferred structure in a seamless and coherent manner. This is later proved in its applications to both large-scale synthetic data sets and Saccharomyces Cerevisiae data sets. The analytical and experimental results reveal the varied characteristic of different types of data and refelct their discriminative ability in terms of identifying direct gene interactions.
Language eng
DOI 10.1515/jib-2008-99
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2008, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30125522

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