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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 [1]. Performance of the proposed algorithm is comparable to that of the techniques which yielded the best results in the DREAM2 challenge competition.