TY - JOUR AU - Rau, Andrea AU - Jaffrézic, Florence AU - Nuel, Grégory PY - 2013 DA - 2013/10/31 TI - Joint estimation of causal effects from observational and intervention gene expression data JO - BMC Systems Biology SP - 111 VL - 7 IS - 1 AB - In recent years, there has been great interest in using transcriptomic data to infer gene regulatory networks. For the time being, methodological development in this area has primarily made use of graphical Gaussian models for observational wild-type data, resulting in undirected graphs that are not able to accurately highlight causal relationships among genes. In the present work, we seek to improve the estimation of causal effects among genes by jointly modeling observational transcriptomic data with arbitrarily complex intervention data obtained by performing partial, single, or multiple gene knock-outs or knock-downs. SN - 1752-0509 UR - https://doi.org/10.1186/1752-0509-7-111 DO - 10.1186/1752-0509-7-111 ID - Rau2013 ER -