Objective: A gene interaction network, along with its related biological features, has an
important role in computational biology. Bayesian network, as an efficient model, based on
probabilistic concepts is able to exploit known and novel biological casual relationships between
genes. The success of Bayesian networks in predicting the relationships greatly depends on
Methods: K-mers have been applied as the prominent features to uncover the similarity between
genes in a specific pathway, suggesting that this feature can be applied to study genes
dependencies. In this study, we propose k-mers (4,5 and 6-mers) highly correlated with epigenetic
modifications, including 17 modifications, as a new prior for Bayesian inference in the gene
Result: Employing this model on a network of 23 human genes and on a network based on 27
genes related to yeast resulted in F-measure improvements in different biological networks.
Conclusion: The improvements in the best case are 12%, 36%, and 10% in the pathway, coexpression,
and physical interaction, respectively.