Our paper at ISMB 2015 addresses a challenging task of inferring gene networks by taking into consideration potentially many data sets. Importantly, these data sets might be nonidentically distributed and can follow any combination of exponential family distributions. To tackle this challenge we develop an efficient Markov network model that achieves fusion by reusing latent model parameters.
Empirical studies on cancer genome data sets show an advantage of joint inference over separate network inference and the merits of incorporating information about the underlying data distribution into inference.
