Inference of Gene Regulatory Network

Typically, the nodes in GRN can be DNA, RNA, protein, gene and complexes of these.

There are several computational approaches to infer GRNs: Boolean network, probabilistic Boolean network, Bayesian network (a directed acyclic graph), dynamic Bayesian network, ordinary differential equations…

The interactions between nodes can be direct or indirect in terms of methodology adopted.

Our interest is to use Boolean network model to infer GRN. The interactions between nodes are represented by Boolean functions.

We refer to logic regression methodology and the bagging version of logic regression. The R code of LogicFS is available from Bioconductor. A useful R package for manipulating Boolean network can be found at BoolNet.

Inferring GRNs based on ensemble logic regression

To infer a Boolean network, you can follow my github at: