TY - JOUR AU - Zou, Cunlu AU - Feng, Jianfeng PY - 2009 DA - 2009/04/24 TI - Granger causality vs. dynamic Bayesian network inference: a comparative study JO - BMC Bioinformatics SP - 122 VL - 10 IS - 1 AB - In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results. SN - 1471-2105 UR - https://doi.org/10.1186/1471-2105-10-122 DO - 10.1186/1471-2105-10-122 ID - Zou2009 ER -