Combinational logic network for digitally coded gene expression of gastric cancer

In general, Boolean networks have been addressed in time-series datasets. However, in the recent field of next-generation sequencing-based cancer genomics, cross-sectional data sets having enormous numbers of patients have been accumulated. Here, we deal with representation of cross-sectional datasets using Boolean networks, and specifically, combinational logic network approach. We then applied the approach to a real cancer patient dataset, demonstrating the feasibility of using Boolean networks in graphical representation of cross-sectional datasets.