Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data

Large-scale high-dimensional omics data sets have been generate to survey complex biological systems. However, it is a challenge how to integrate multiple dimensions of biological data to biological causal networks where comprehensive knowledge can be derived in contexts. We developed a RIMBANet (Reconstructing Integrative Molecular Bayesian Networks) method to integrate diverse biological data. In this chapter, we disseminate results of applying our RIMBANet method on a series of simulated datasets. Two sets of networks are inferred with or without integrating genetic markers with gene expression data. We show that integration of genetic data into network reconstruction using RIMBANet approach improves network construction accuracy. Furthermore, false-positive links in reconstructed networks are not randomly distributed. More than 80 % of them connect nodes that are indirect neighbors.

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