Reconstructing biological networks using low order partial correlation

One major challenge of systems biology is to infer biological networks. Classical graphical modeling methods that measure full conditional relationships between random variables may lead to unreliable results. This is partly due to the singular matrix problem when the number of variables exceeds the number of the samples. In this paper, we propose a low order partial correlation method to address this problem, trading off the small bias introduced by the low order constraint for the more reliable approximation of the network structure. Simulation results show that our proposed method works well under various conditions commonly seen in real applications and the inferred network faithfully uncovers the conditional independence relations among variables.