USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION

Abstract

Bayesian networks, one of the most widely used techniques to understand or predict the future by making use of current or previous data, have gained credence over the last decade for their ability to simulate large gene expression datasets to track and predict the reasons for changes in biological systems. In this work, we present a dynamic Bayesian model with gene annotation scores such as the gene characterization index (GCI) and the GenCards inferred functionality score (GIFtS) to understand and assess the prediction performance of the model by incorporating prior knowledge. Time-course breast cancer data including expression data about the genes in the breast cell-lines when treated with doxorubicin is considered for this study. Bayes server software was used for the simulations in a dynamic Bayesian environment with 8 and 19 genes on 12 different data combinations for each category of gene set to predict and understand the future time- course expression profiles when annotation scores are incorporated into the model. The 8-gene set predicted the next time course with r>0.95, and the 19-gene set yielded a value of r>0.8 in 92% cases of the simulation experiments. These results showed that incorporating prior knowledge into the dynamic Bayesian model for simulating the time- course expression data can improve the prediction performance when sufficient apriori parameters are provided.