A Bayes random field approach for integrative large-scale regulatory network analysis.
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.
Department of Computer Science, University of Warwick, CV4 7AL Coventry, UK. yina@dcs.warwick.ac.uk
Yinyin
Y
Yuan
Chang-Tsun
CT
Li
D001499:Bayes Theorem; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism