Biomarker Prioritisation through Interpretable Machine Learning and Multi-Omics Storage: A Cross-Site Validation Agenda

Abstract

Multi-omics studies routinely combine assay tables with inconsistent metadata and batch effects that can distort biomarker rankings. An evaluation that overlooks these constraints may reach conclusions that cannot be reproduced. This study aims to assess the robustness of interpretable biomarker prioritisation to batch variation and data-provenance uncertainty. The evaluation would follow 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 cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. The technical pipeline must remain auditable: prior work on storage operations provides system-level context [1], while a second source identifies infrastructure conditions relevant to data continuity [2], rather than patient-level outcomes. 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]. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].