Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

• Published in: BMC bioinformatics Vol. 12; no. 1; p. 359
• Main Authors: Gao, Shouguo, Wang, Xujing
• Format: Journal Article
• Language: English
• Published: London BioMed Central 31.08.2011; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Animals; Bayes Theorem; Bayesian statistical decision theory; Bias; Bioinformatics; Biomedical and Life Sciences; Computational biology; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Diabetes; Gene expression; Gene Expression Profiling - methods; Life Sciences; Markov analysis; Markov Chains; Methodology; Methodology Article; Mice; Microarrays; Models, Genetic; Ontology; Pancreas - cytology; Pancreas - metabolism; Probability; Proteins; PubMed; Random variables; Reservoirs; Reverse engineering; Saccharomyces cerevisiae - cytology; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae Proteins - genetics; Standard deviation; Studies; Transcriptome analysis; Yeast; Yeasts; United States; Markov Chain Monte Carlo; Gene Pair; Bayesian Network; Gene Ontology; Gene Expression Data
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-12-359

Background Bayesian 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. Results We 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. Conclusion our 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.