Biomarker Prioritisation through Interpretable Machine Learning and Multi-Omics Storage: A Reproducibility-Centred Evaluation
DOI:
https://doi.org/10.66687/f95hsz93Keywords:
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 reproducibility extension records dataset versions, model settings, exclusion rules and provenance before any held-out evaluation. 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. An independent replication exercise would repeat the main analysis from the documented artefacts and quantify disagreements. 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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