Incorporating Literature Knowledge in Bayesian Network for Inferring Gene Networks with Gene Expression Data

The reconstruction of gene networks from microarray gene expression has been a challenging problem in bioinformatics. Various methods have been proposed for this problem. The incorporation of various genomic and proteomic data has been shown to enhance the learning ability in the Bayesian Network (BN) approach. However, the knowledge embedded in the large body of published literature has not been utilized in a systematic way. In this work, prior knowledge on gene interaction was derived based on the statistical analysis of published interactions between pairs of genes or gene products. This information was used (1) to construct a structure prior and (2) to reduce the search space in the BN algorithm. The performance of the two approaches was evaluated and compared with the BN method without prior knowledge on two time course microarray gene expression data related to the yeast cell cycle. The results indicate that the proposed algorithms can identify edges in learned networks with higher biological relevance. Furthermore, the method using literature knowledge for the reduction of the search space outperformed the method using a structure prior in the BN framework.