Cancer classification based on gene expression using neural networks | Zendy

Hanping Hu | Zendy; Zhenxing Niu | Zendy; Yanping Bai | Zendy; Xiaohua Tan | Zendy

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Cancer classification based on gene expression using neural networks

Author(s) -

Hanping Hu,

Zhenxing Niu,

Yanping Bai,

Xiaohua Tan

Publication year - 2015

Publication title -

genetics and molecular research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.356

H-Index - 48

ISSN - 1676-5680

DOI - 10.4238/2015.december.21.33

Subject(s) - self organizing map , artificial neural network , support vector machine , artificial intelligence , pattern recognition (psychology) , computer science , gene , machine learning , biology , genetics

Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.

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