Biomarker Prioritisation through Interpretable Machine Learning and Multi-Omics Storage: An Audit-Ready Methods Framework
DOI:
https://doi.org/10.66687/bawqk311Keywords:
Artificial Intelligence, Machine Learning, Medical Informatics, Decision Support Systems, Clinical, Reproducibility of ResultsAbstract
Multi-omics studies routinely combine assay tables with inconsistent metadata and batch effects that can distort biomarker rankings. This gap has consequences for interpreting performance outside carefully curated evaluation settings. 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 audit-focused extension preserves processing logs, model decisions and exception handling for independent review. 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. Uncertain outcomes would be adjudicated with a predefined rubric, rather than silently recoded as successes. 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].
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