A Fast Multivariate Feature-Selection/Classification Approach for Prediction of Therapy Response in Multiple Sclerosis

Recombinant interferon beta (IFNp) is one of the most commonly prescribed treatments for multiple sclerosis; however, the treatment results in partial success producing no benefit in almost half of the patients. We address the problem of identifying minimal and robust sets of molecular biomarkers that are able to present predictive models of response to treatment in multiple sclerosis patients. To achieve this, we utilize a multivariate feature selection and classification framework; OSeMA (orthogonal search model analysis) integrates fast orthogonal search algorithm for feature selection and discriminant analysis for classification. Feature-selection and classification performance of OSeMA are evaluated through comparative studies with two wrapper-approach feature-selection/classification systems. It is demonstrated that the feature-selection of OSeMA significantly reduces the computational time of exhaustive searches while identifying complex gene-gene relationships. Utilizing OSeMA, we are able to construct classification models that are highly predictive of therapy response in MS patients, based on their gene expression data acquired prior to initiation of IFNp treatment