A combinational logic network based on binarised gene expression in gastric cancer. (2017)

• Record Type: Journal Article
• Authors: Park, Sungjin; Nam, Seungyoon
• Is Part Of: International journal of data mining and bioinformatics. Volume 17:Number 3(2017)
• Page Start: 206
• Page End: 216
• Publication Date: 2017
• Subjects: Boolean logic model -- network model -- gastric cancer; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

In general, gene circuit networks are employed for analysing time-dependent gene expression data sets, known as 'time-series'. However, in analysing cancer genomics data acquired by the recent technology of next-generation sequencing data sets, which are measured once at a particular point in time (static), with enormous numbers of patients have accumulated. Here, we present a combinational logic network, with static gene expression data sets, to combine the structural compositions and all values of samples using Boolean Algebra expression, rather than using representative values. We then attempt to validate this approach by applying it to a real cancer patient data set, demonstrating the feasibility of using combinational logic networks for graphically representing static gene expression data sets.