Support vector machine (SVM) is a popular classification method for the analysis of wide
range of data including big data. Many SVM methods with feature selection have been
developed under frequentist regularization or Bayesian shrinkage frameworks. On the other
hand, the importance of incorporating a priori known biological knowledge, such as gene
pathway information which stems from the gene regulatory network, into the statistical
analysis of genomic data has been recognized in recent years. In this article, we propose a …

Support vector machines (SVM) is a popular classification method for analysis of high
dimensional data such as genomics data. Recently, new SVM methods have been
developed to achieve variable selection through either frequentist regularization or
Bayesian shrinkage. The Bayesian framework provides a probabilistic interpretation for SVM
and allows direct uncertainty quantification. In this dissertation, we develop four knowledge-
guided SVM methods for the analysis of high dimensional data.