title: Cancer classification based on gene expression using neural networks.
abstract: 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.
authors.affiliation: School of Science, North University of China, Taiyuan, Shanxi, China.
authors.forename: H P
authors.initials: HP
authors.lastname: Hu
authors.affiliation: School of Science, North University of China, Taiyuan, Shanxi, China.
authors.forename: Z J
authors.initials: ZJ
authors.lastname: Niu
authors.affiliation: School of Science, North University of China, Taiyuan, Shanxi, China.
authors.forename: Y P
authors.initials: YP
authors.lastname: Bai
authors.affiliation: School of Science, North University of China, Taiyuan, Shanxi, China.
authors.forename: X H
authors.initials: XH
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D003110:Colonic Neoplasms / Q000145:classification / Q000235:genetics* / Q000473:pathology; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016571:Neural Networks, Computer