Abstract
It is well known that incorporating prior knowledge improves gene regulatory network reconstruction from data. Two types of prior knowledge can be given for the gene regulatory network inference - known interactions (edge priors) and known absence of interactions (non-edge priors). However, previous studies have focused mainly on edge priors. This paper shows that the edge priors give only limited improvement. Moreover, non-edge priors are crucial for better overall performance and their effect dominates edge priors at larger data samples. The studies are carried out on two real networks and a computationally tractable synthetic network, using Bayesian network framework. Further, a method to obtain large numbers of non-edge priors for real gene regulatory networks is presented.
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Nair, A., Chetty, M., Wangikar, P.P. (2014). Significance of Non-edge Priors in Gene Regulatory Network Reconstruction. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_56
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DOI: https://doi.org/10.1007/978-3-319-12637-1_56
Publisher Name: Springer, Cham
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