  In the analysis of binary disease classification, single biomarkers might not
have significant discriminating power and multiple biomarkers from a large set
of biomarkers should be selected. Numerous approaches exist, but they merely
work well for mean differences in biomarkers between cases and controls.
Biological processes are however much more heterogeneous, and differences could
also occur in other distributional characteristics (e.g. variances, skewness).
Many machine learning techniques are better capable of utilizing these higher
order distributional differences, sometimes at cost of explainability.
  In this study we propose quantile based prediction (QBP), a binary
classification method that is based on the selection of multiple continuous
biomarkers. QBP generates a single score using the tails of the biomarker
distributions for cases and controls. This single score can then be evaluated
by ROC analysis to investigate its predictive power.
  The performance of QBP is compared to supervised learning methods using
extensive simulation studies, and two case studies: major depression disorder
and trisomy. Simultaneously, the classification performance of the existing
techniques in relation to each other is assessed. The key strengths of QBP are
the opportunity to select relevant biomarkers and the outstanding
classification performance in the case biomarkers predominantly show variance
differences between cases and controls. When only shifts in means were present
in the biomarkers, QBP obtained an inferior performance. Lastly, QBP proved to
be unbiased in case of absence of disease relevant biomarkers and outperformed
the other methods on the MDD case study.
  More research is needed to further optimize QBP, since it has several
opportunities to improve its performance. Here we wanted to introduce the
principle of QBP and show its potential.
