Biomarker Prioritisation through Interpretable Machine Learning and Multi-Omics Storage: Evidence, Failure Modes and Deployment Trade-offs

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. The research question is whether it is possible to assess the robustness of interpretable biomarker prioritisation to batch variation and data-provenance uncertainty. Previous work provides context for sampling, input validation and study boundaries [3–7]. The planned analysis uses a nested cross-cohort feasibility protocol using harmonised transcriptomic and other available assay data. 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. Under matched evaluation conditions, the protocol compares sparse statistical models, tree-based feature selectors and explanation-informed ranking procedures. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The evidence-mapping component examines why a successful result in one technical environment may fail elsewhere. Performance will be judged using feature-selection stability, held-out cohort prediction and agreement of pathways across resampling runs. Interpretation will account for laboratory batch effects, overlapping subjects, incomplete sample metadata and the interpretability of selected biomarkers. Stress tests would distinguish outright failures, reduced confidence and cases where a simpler baseline is preferable. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].