TITLE: A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery 

ABSTRACT: 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 diversity of fictional data to examine the higher-order
composite effect of multi-omics datasets. We address a robust kernel-centered
Gram matrix to estimate the model parameters accurately. We also propose a
robust score test to assess the marginal and joint Hadamard product of features
from multi-omics data. We apply our proposed approach to a multi-omics dataset
of osteoporosis (OP) from Caucasian females. Experiments demonstrate that the
proposed approach effectively identifies the inter-related risk factors of OP.
With solid evidence (p-value = 0.00001), biological validations, network-based
analysis, causal inference, and drug repurposing, the selected three triplets
((DKK1, SMTN, DRGX), (MTND5, FASTKD2, CSMD3), (MTND5, COG3, CSMD3)) are
significant biomarkers and directly relate to BMD. Overall, the top three
selected genes (DKK1, MTND5, FASTKD2) and one gene (SIDT1 at p-value= 0.001)
significantly bond with four drugs- Tacrolimus, Ibandronate, Alendronate, and
Bazedoxifene out of 30 candidates for drug repurposing in OP. Further, the
proposed approach can be applied to any disease model where multi-omics
datasets are available.
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 diversity of fictional data to examine the higher-order
composite effect of multi-omics datasets. We address a robust kernel-centered
Gram matrix to estimate the model parameters accurately. We also propose a
robust score test to assess the marginal and joint Hadamard product of features
from multi-omics data. We apply our proposed approach to a multi-omics dataset
of osteoporosis (OP) from Caucasian females. Experiments demonstrate that the
proposed approach effectively identifies the inter-related risk factors of OP.
With solid evidence (p-value = 0.00001), biological validations, network-based
analysis, causal inference, and drug repurposing, the selected three triplets
((DKK1, SMTN, DRGX), (MTND5, FASTKD2, CSMD3), (MTND5, COG3, CSMD3)) are
significant biomarkers and directly relate to BMD. Overall, the top three
selected genes (DKK1, MTND5, FASTKD2) and one gene (SIDT1 at p-value= 0.001)
significantly bond with four drugs- Tacrolimus, Ibandronate, Alendronate, and
Bazedoxifene out of 30 candidates for drug repurposing in OP. Further, the
proposed approach can be applied to any disease model where multi-omics
datasets are available.
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
http://arxiv.org/abs/2201.05060v1