Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. We developed a semi-supervised network reconstruction algorithm that enables the synthesis of information from partially known networks with time course gene expression data. We adapted partial least square-variable importance in projection (VIP) for time course data and used reference … Continue reading Post-PLSR: network inference using time-series data