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BY-NC-ND 4.0 license Open Access Published by De Gruyter October 18, 2016

A Bayes Random Fields Approach for Integrative Large-Scale Regulatory Network Analysis

  • Yinyin Yuan EMAIL logo and Chang-Tsun Li

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 reflect their discriminative ability in terms of identifying direct gene interactions.

Published Online: 2016-10-18
Published in Print: 2008-6-1

© 2008 The Author(s). Published by Journal of Integrative Bioinformatics.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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