Data Shared Neighbourhood Selection for multi-condition network inference

2026-08-24

AI Summary

提出Data Shared Neighbourhood Selection方法，结合共享Lasso分解和邻域选择，用于多条件下网络推断，评估生物标志物关联结构的变化。

Abstract

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.

Problem

• external stresses
• biomarkers
• correlation structures
• disease risk
• molecular associations

Innovation

• Data Shared Neighbourhood Selection
• Neighbourhood Selection
• Data Shared Lasso
• Stability Approach to Regularisation Selection
• Joint Network Inference