Screening of feature genes in distinguishing different types of breast cancer using support vector machine.

ABSTRACT:

Objective

To screen the feature genes in estrogen receptor-positive (ER+) breast cancer in comparison with estrogen receptor-negative (ER-) breast cancer.

Methods

Nine microarray data of ER+ and ER- breast cancer samples were collected from Gene Expression Omnibus database. After preprocessing, data in five training sets were analyzed using significance analysis of microarrays to screen the differentially expressed genes (DEGs). The DEGs were further analyzed via support vector machine (SVM) function in e1071 package of R to construct a SVM classifier, the efficacy of which was verified by four testing sets and its combination with training sets using a leave-one-out cross-validation. Feature genes obtained by SVM classifier were subjected to function- and pathway-enrichment

SUBMITTER: Wang Q 

PROVIDER: S-EPMC4556031 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature