A Network Topology Scaling Method for Improving Network Comparison Using Colon Cancer Transcriptome Data 


Vol. 49,  No. 8, pp. 646-654, Aug.  2022
10.5626/JOK.2022.49.8.646


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  Abstract

Various research methods have been proposed based on gene expression information in the disease analysis model. In cancer transcriptome data analysis, methods of discovering hidden characteristics based on pathways are useful for the interpretation of results. In this study, the gene correlation network in the pathway unit was compared and analyzed based on the gene co-expression data. If there is a difference in the size of the two networks to be compared, the bias of the amount of information results in biased network information on a larger scale. To resolve this bias, the network of patients from different backgrounds was adjusted using the same amount of information in the network configuration. Normalized networks applied comparative analysis of important gene groups using the characteristics of biological networks, normalized 202 pathways networks using data of subtypes of total 4 types of colon cancer, and identified 5 pathways with specific results among subspecies.


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  Cite this article

[IEEE Style]

E. Han and I. Jung, "A Network Topology Scaling Method for Improving Network Comparison Using Colon Cancer Transcriptome Data," Journal of KIISE, JOK, vol. 49, no. 8, pp. 646-654, 2022. DOI: 10.5626/JOK.2022.49.8.646.


[ACM Style]

Eonyong Han and Inuk Jung. 2022. A Network Topology Scaling Method for Improving Network Comparison Using Colon Cancer Transcriptome Data. Journal of KIISE, JOK, 49, 8, (2022), 646-654. DOI: 10.5626/JOK.2022.49.8.646.


[KCI Style]

한언용, 정인욱, "복수 개의 대장암 유전자 상관관계 네트워크 간 비교 분석 향상을 위한 네트워크 스케일링 방법," 한국정보과학회 논문지, 제49권, 제8호, 646~654쪽, 2022. DOI: 10.5626/JOK.2022.49.8.646.


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