The mean for the Poisson distribution is parameterized by the sum of feature promiscuity (number of SecMs g connected to a feature) μf , interactor promiscuity (number of SecPs with a feature finteracting with a SecM) μg , the interaction synergy sf,g and an intercept variable. The feature promiscuity μ_fquantifies the probability of a feature f to partake in an interaction with any secMs. Likewise, the interactor promiscuityμ_g measures the tendency for a secM g to interact with any features. The coefficient of interest, the interaction synergy s _f,g between f and g , quantifying the degree to which f and g interact more than by random chance, quantifies the degree to which f and g interact more than by random chance. In previous work, this approach has correctly estimated epistasis intensity (Shen et al., 2017). To better regularize the promiscuities, their Bayesian priors are all normally distributed around 0, with their variances parameterized by the hyper priors 𝜎_f , 𝜎_g and 𝜎_s which follow an exponential distribution. The intercept ɑ is parameterized by a standard normal distribution. We used the rethinking R package (McElreath, 2020) to construct the model and sample the coefficients.