A Bayes Random Field Approach for Integrative Large-Scale Regulatory Network Analysis
Yuan, Y. and Li, C.-T. (2008) A Bayes Random Field Approach for Integrative Large-Scale Regulatory Network Analysis. Journal of Integrative Bioinformatics, 5 (2). ISSN 1613-4516Full text not available from this repository.
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 di�erent types of data and refelct their discriminative ability in terms of identifying direct gene interactions.
|Uncontrolled Keywords:||pcav image|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Faculty of Science > Computer Science|
|Depositing User:||Nadeem Chaudhary|
|Date Deposited:||25 May 2012 11:38|
|Last Modified:||25 May 2012 14:26|
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