Biomarker Prioritisation through Interpretable Machine Learning and Multi-Omics Storage

Abstract

Multi-omics studies routinely combine assay tables with inconsistent metadata and batch effects that can distort biomarker rankings. 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 design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. 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]. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].