Using Class-Specific Feature Selection for Cancer Detection with Gene Expression Profile Data of Platelets.

ABSTRACT: A novel multi-classification method, which integrates the elastic net and probabilistic support vector machine, was proposed to solve this problem in cancer detection with gene expression profile data of platelets, whose problems mainly are a kind of multi-class classification problem with high dimension, small samples, and collinear data. The strategy of one-against-all (OVA) was employed to decompose the multi-classification problem into a series of binary classification problems. The elastic net was used to select class-specific features for the binary classification problems, and the probabilistic support vector machine was used to make the outputs of the binary classifiers with class-specific features comparable. Simulation data and gene expression profile data were intended to verify

SUBMITTER: Yuan LM 

PROVIDER: S-EPMC7085688 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature