AI 中文总结
针对多条件下分子关联结构的疾病风险评估问题，提出DSNS联合网络推断方法，在模拟及肺癌蛋白质组数据中表现优异，可识别关键生物标志物的关联变化。
AI 中文摘要
外部应激既可能影响特定生物标志物的循环水平，也会扰乱分子实体间的相关结构，目前两类失调对后续疾病风险的贡献尚未得到评估。我们提出数据共享邻域选择（Data Shared Neighbourhood Selection，DSNS），这是一种联合网络推断方法，用于估计相关条件间保留的及改变的条件关联结构。DSNS将邻域选择与数据共享Lasso分解相结合，将每个节点的回归系数表示为共享分量与稀疏的条件特异性偏差之和，在保留邻域选择计算优势的同时，实现了分子关联的可解释分解。我们还将正则化选择的稳定性方法（Stability Approach to Regularisation Selection，StARS）适配到该双参数联合估计场景。在涉及稀疏、枢纽型及重连扰动机制的模拟中，DSNS在两条件下与最优联合估计方法表现相当，且随条件数量增加时性能优于这些方法，同时比图Lasso框架快得多。将其应用于EPIC-Italy和NOWAC队列中未来肺癌病例及匹配对照的诊断前炎症蛋白质组数据，DSNS突出了已确立的肺癌风险标志物CDCP1和IL10参与的改变关联，以及风险模型未选中蛋白质的差异关联。
英文摘要
External stresses may affect both the circulating levels of specific biomarkers and disturb the correlation structures across molecular entities. The contribution of both types of dysregulations to the subsequent risk of disease are yet to be evaluated. We propose Data Shared Neighbourhood Selection (DSNS), a joint network inference method for estimating preserved and altered conditional association structures across related conditions. DSNS combines neighbourhood selection with the Data Shared Lasso decomposition, representing each nodewise regression coefficient as the sum of a shared component and a sparse condition-specific deviation. This provides an interpretable decomposition of molecular associations while retaining the computational advantages of neighbourhood selection. We also adapt the Stability Approach to Regularisation Selection (StARS) to this two-parameter joint estimation setting. In simulations involving sparse, hub-based and rewiring perturbation mechanisms, DSNS matched the best joint estimation methods for two conditions and outperformed them as the number of conditions increased, while remaining substantially faster than graphical lasso frameworks. Applied to prediagnostic inflammatory proteomic data from future lung cancer cases and matched controls in the EPIC-Italy and NOWAC cohorts, DSNS highlighted altered associations involving CDCP1 and IL10, two established lung cancer risk markers, as well as differential associations involving proteins not selected by risk models.
