Open AccessCancer classification based on gene expression using neural networks
Author(s) -
Hu Hp,
Niu Zj,
Bai Yp,
Tan Xh
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.