资 源 简 介
We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatory network inference on these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that if a transcription factor is knocked out artificially from the gene network, the disruptions to its downstream targets should be greater than non-targets.
