BINDER infers gene regulatory networks by constructing two strata: a primary stratum and an auxiliary stratum. The primary stratum is composed of coexpression data from a primary organism of interest and is supplemented by auxiliary data in the form of motif predictions and known orthologous regulator-target interactions from a proxy organism. BINDER implements a Bayesian hierarchical model that appositely models the type and structure of both this primary and auxiliary data to infer the probability of a regulatory interaction between a regulator-target candidate pair. The auxiliary data inform the prior distributions and the posterior distributions are updated by accounting for the primary coexpression data in a novel, apposite bivariate likelihood function.
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