Inference of gene-regulatory networks using message-passing algorithms

We present an application of message-passing techniques to gene regulatory network inference. The network inference is posed as a constrained linear regression problem, and solved by a distributed computationally efficient message-passing algorithm. Performance of the proposed algorithm is tested on gold standard data sets and evaluated using metrics provided by the DREAM2 challenge. Performance of the proposed algorithm is comparable to that of the techniques which yielded the best results in the DREAM2 challenge competition.