Automatically suggest suitable threshold for marginal regulator activities.
The algorithm fits a mixture of a beta(1,beta) and beta(alpha,1) distribution to observed marginal regulator activities. Based on this mixture a cutoff is chosen such that the expected false positive rate is below a defined threshold.
marginal probability obtained from
threshold for accepted false positive rate
a cutoff for marginal activity probabilities
Froehlich, H. and Klau, G. (2013). Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks. In: Beissbarth, T., Kollmar, M., Leha, A., Morgenstern, B., Schultz, A.-K., Waack, S., and Wingender, E., editors, Proc. German Conference on Bioinformatics, Open Access Series in Informatics, pages 46 - 55. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Dagstuhl Publishing, Germany.
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