Abstract
BackgroundBayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.ResultsWe introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.Conclusionour new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.
Highlights
Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data
We will demonstrate that including the prior knowledge significantly improves the performance of BN modeling of gene expression data
This way, the edges between the strongly-related gene pairs have higher chance to be proposed as part of the candidate network
Summary
Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. Expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. The time course gene expression study offers an ideal data source for transcription regulatory network modeling. Incorporating other types of data and existing knowledge of gene relationships into the network modeling process is a practical approach to overcome some of these problems. It has been proven that data integration and useful bias with relevant knowledge can improve the network prediction accuracy from gene expression data [6,7]. The sound probabilistic schematics allow BN to deal with the inherent stochasticity in gene expressions and the noise brought in by the microarray technology. BN is capable of integrating prior knowledge into the system in a natural way [9,10]
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