An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset

摘要: In recent years, a few researchers have challenged past dogma and suggested methods (such as the IC algorithm) for inferring causal relationship among variables using steady state observations. this paper, we present modified (mIC) algorithm that uses entropy to test conditional independence combines data with partial prior knowledge of topological ordering in gene regulatory network, jointly learning genes. We evaluate our mIC simulated data. The results show precision recall rates are significantly improved compared algorithm. Finally, apply microarray melanoma. identified important relations associated WNT5A, playing an role melanoma, verified by literatures.