Gene regulatory network inference schematic.

(A) Our network inference algorithm takes as input a gene expression matrix, X, and a prior on network structure and outputs regulatory hypotheses of regulator-target interactions. (B) Using priors on network topology and gene expression data, we estimate transcription factor activities (TFA), and subsequently model gene expression as a function of these activities. (C) We use several possible sources of prior information on network topology. (D) Prior information is encoded in a matrix P, where positive and negative entries represent known activation and repression respectively, whereas zeros represent absence of known regulatory interaction. To estimate hidden activities, we consider X = PA (top), where the only unknown is the activities. Of note, a time-delay is implemented for time-series experiments (bottom). (E) Finally, for each gene, we find regulators that influence its expression using regularized linear regression. We either learn these influences, or weights, for each dataset independently, single-task learning (top), or jointly through multi-task learning (bottom).