Analysis of anti-cancer cytokines by Apriori algorithm, decision tree, and SVM

Author

Yoohyeon Cho ; Ahn, Yeahji ; Subin Yoon ; Jinwon Kwon ; Taeseon Yoon

Author_Institution

Dept. of Natural Sci., Hankuk Acad. of Foreign Studies, Yongin, South Korea

fYear

2015

fDate

9-11 Feb. 2015

Firstpage

232

Lastpage

237

Abstract

Cancer is currently a major cause of death, which resulted in great interest in the mechanisms of this disease, and how to prevent or cure it. Certain cytokines are spotlighted to be a key to solving this problem, since they play a role in the immune system against cancer. Thus, our goal is to analyze various cytokines and to mine their rules. In this study, we aimed to mine a common rule between anti-cancer cytokines: Interferon-gamma (INF-gamma), Tumor Necrosis Factor (TNF), Transforming Growth Factor beta (TNF-beta), Interleukin-2 (IL-2) and Interleukin-10 (IL-10). We analyzed their mRNA sequences using three kinds of algorithms: Apriori, Decision tree, and Support Vector Machine (SVM). We hope to contribute to finding new rules or hints to determine whether a certain cytokine may have anti-cancer properties, and thus help further studies concerning this subject.

Keywords

RNA; cancer; decision trees; medical computing; proteins; support vector machines; IL-10; IL2; INF-gamma; SVM; TNF; TNF-beta; anti-cancer cytokines; apriori algorithm; decision tree; interferon-gamma; interleukin-10; interleukin-2; mRNA sequences; rule mining; transforming growth factor beta; tumor necrosis factor; Algorithm design and analysis; Amino acids; Cancer; Decision trees; Polynomials; Support vector machines; Tumors; Anti-cancer Cytokines; Apriori; Cancer; Decision Tree; Support Vector Machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Big Data and Smart Computing (BigComp), 2015 International Conference on

Conference_Location

Jeju

Type

conf

DOI

10.1109/35021BIGCOMP.2015.7072836

Filename

7072836