Biomarker Prioritisation through Interpretable Machine Learning and Multi-Omics Storage: A Resilience-Oriented Study Protocol

Abstract

Multi-omics studies routinely combine assay tables with inconsistent metadata and batch effects that can distort biomarker rankings. The difficulty is particularly visible when a system is transferred to a new setting. To address this, the study will assess the robustness of interpretable biomarker prioritisation to batch variation and data-provenance uncertainty. The research design centres on a nested cross-cohort feasibility protocol using harmonised transcriptomic and other available assay data. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast sparse statistical models, tree-based feature selectors and explanation-informed ranking procedures. The resilience component introduces missing data, unexpected interruptions and recovery dependencies as controlled disturbances. Enterprise-storage sources are used to specify constraints on data availability and provenance [1], with complementary infrastructure evidence defining practical recovery safeguards [2]. These citations are not treated as clinical effectiveness evidence. Assessment will focus on feature-selection stability, held-out cohort prediction and agreement of pathways across resampling runs. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address laboratory batch effects, overlapping subjects, incomplete sample metadata and the interpretability of selected biomarkers. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. Outcomes would be assessed before and after each disturbance, including time needed to restore valid operation. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].