Hadamard Kernel SVM with applications for breast cancer outcome predictions.

ABSTRACT:

Background

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation.

Results

Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the

SUBMITTER: Jiang H 

PROVIDER: S-EPMC5763304 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature