We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.
McGoff, K. A., Guo, X., Deckard, A., Kelliher, C. M., Leman, A. R., Francey, L. J., … Harer, J. L. (2016). The Local Edge Machine: Inference of dynamic models of gene regulation. Genome Biology, 17(1). https://doi.org/10.1186/s13059-016-1076-z