In practice, usually multiple biomarkers are measured on the same subject for disease diagnosis. Combining biomarkers into a single score could improve diagnostic accuracy. However, methods to combine multiple biomarkers are either distribution-dependent or computationally clumsy. This problem becomes more challenging when the number of observations is not order of magnitude greater than the number of variables, especially when the involved biomarkers are relatively weak. Furthermore, combination methods usually use AUC (or PAUC) as objective function, however AUC (or PAUC) can have serious drawbacks. This talk will present some recent developments in this field addressing several key issues in biomarker combination within ROC framework.