Title: A combinational logic network based on binarised gene expression in gastric cancer

Authors: Sungjin Park; Seungyoon Nam

Addresses: Department of Genome Medicine and Science, College of Medicine, Gachon University, Incheon 21565, Korea; Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon 21565, Korea ' Department of Genome Medicine and Science, College of Medicine, Gachon University, Incheon 21565, Korea; Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon 21565, Korea

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.

Keywords: Boolean logic model; network model; gastric cancer.

DOI: 10.1504/IJDMB.2017.085279

International Journal of Data Mining and Bioinformatics, 2017 Vol.17 No.3, pp.206 - 216

Received: 04 Apr 2017
Accepted: 06 Apr 2017
Published online: 19 Jul 2017 *

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