Binary disease prediction using tail quantiles of the distribution of continuous biomarkers
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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.
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