A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery. (arXiv:2201.05060v1 [stat.ML])

Many statistical machine approaches could ultimately highlight novel features
of the etiology of complex diseases by analyzing multi-omics data. However,
they are sensitive to some deviations in distribution when the observed samples
are potentially contaminated with adversarial corrupted outliers (e.g., a
fictional data distribution). Likewise, statistical advances lag in supporting
comprehensive data-driven analyses of complex multi-omics data integration. We
propose a novel non-linear M-estimator-based approach, "robust kernel machine
regression (RobKMR)," to improve the robustness of statistical machine
regression and the …

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