A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer.

ABSTRACT: Gastric cancer (GC) ranks fifth in terms of incidence and third in terms of tumor mortality worldwide. The present study was designed to construct a Support Vector Machine (SVM) classifier and risk score system for GC. The GSE62254 (training set) and GSE26253 (validation set 2) datasets were downloaded from the Gene Expression Omnibus database. Furthermore, the gene expression profile of GC (validation set 1) was obtained from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) between recurrent and non‑recurrent samples were determined using the limma package. The feature genes were selected using the Caret package, and an SVM classifier was built using the e1071 package. Using the penalized package, the optimal predictive genes for constructing a risk score system wer

SUBMITTER: Jiang H 

PROVIDER: S-EPMC6896370 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature