Comparison of Gene Co-expression Networks and Bayesian Networks

Inferring genetic networks is of great importance in unlocking gene behaviour, which in turn provides solutions for drug testing, disease resistance, and many other applications. Dynamic network models provide room for handling noisy or missing prelearned data. This paper discusses how Dynamic Bayesian Networks compare against coexpression networks as discussed by Zhang and Horvath [1]. These shall be tested out on the genes of yeast Saccharomyces cerevisiae. A method is then proposed to get the best out of the strengths of both models, namely, the causality inference from Bayesian networks and the scoring method from a modified version of Zhang and Horvath’s method.