This paper presents some essential findings and results on using ranking-based kernels for the analysis and utilization of high dimensional and noisy biomedical data in applied clinical diagnostics. We claim that presented kernels combined with a state-of-the-art classification technique - a Support Vector Machine (SVM) - could significantly improve the classification rate and predictive power of the wrapper method, e.g. SVM. Moreover, the advantage of such kernels could be potentially exploited for other kernel methods and essential computer-aided tasks such as novelty detection and clustering. Our experimental results and theoretical generalization bounds imply that ranking-based kernels outperform other traditionally employed SVM kernels on high dimensional biomedical and microarray data.