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Visualization of gene data using network analysis
Journal of the Korean Data & Information Science Society 2018;29:1421-43
Published online November 30, 2018
© 2018 Korean Data and Information Science Society.

Taerim Lee1

1Department of Information Statistics, Korea National Open University
Correspondence to: Professor, Department of Information Statistics, Korea National Open University, 86 Daehak ro, Jongro gu, Seoul, Korea. E-mail: trlee@knou.ac.kr
This research was supported by Korea National Open University Research Fund.
Received October 22, 2018; Revised November 19, 2018; Accepted November 20, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Objectives: The purpose of this paper is to analyze the relationship among major gene data and clinical data in HCC by network analysis and word cloud. According to the several covariate we can compare the characteristics. Methods: We used network analysis to visualize the relationships and hidden structure among the genes and clinical covariates collected from 2011-2015 SNU Liver Center in Korea with classification tree, survival tree, SDA correlation circle, network graph, wordle. Results: We find that smoking and obesity is the most important factor of causing HCC prognosis. We can visually recognize these results from the network graphs made by network analysis and wordle. Conclusions: We made the age-specific social network graphs between genes and clinical lab data of HCC in Korea across diagnosis and prognosis.
Keywords : Disease network, Korean disease network, network analysis, word-cloud, wordle