A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
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用于骨质疏松多组学生物标志物筛选与药物发现的稳健核机器回归
- 研究问题
- 在多组学数据可能受到异常值污染时，如何稳健估计高阶联合效应、筛选骨质疏松相关生物标志物，并探索药物再利用线索？
- 方法与思路
- 作者提出基于非线性 M 估计的稳健核机器回归，构造稳健中心化 Gram 矩阵，并以得分检验评估边际和联合特征效应。
- 材料陈述的贡献
- 摘要称，该方法在白人女性骨质疏松数据中识别出相关风险因素和基因组合，并结合生物验证及其他分析提出药物再利用线索。
- 阅读关注点
- 应核对污染设定、稳健估计与检验的有效性、多重筛选处理，以及生物验证、因果分析和药物关联分别使用何种证据。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要中的显著性与关联不能确认药物疗效，也不足以支持适用于所有疾病的主张。
原文摘要
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.