Identifying cancer biomarkers by network-constrained support vector machines

TLDR: A network-based approach for cancer biomarker identification, netSVM, is developed, resulting in an improved prediction performance with network biomarkers and several novel hub genes, which may provide new insight to the underlying mechanism of breast cancer metastasis.

Abstract: Background One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.