ArticleFrontiers in systems biology2026
A systems microbiology framework for reproducible multi-dataset omics integration with application to long COVID.
Article in Frontiers in systems biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Integrative systems microbiology increasingly relies on algorithmic approaches capable of extracting biologically meaningful patterns from heterogeneous and often high dimensional, low-sample-size (HDLSS) biological datasets. A major obstacle in this setting is the instability of inferred molecular signatures across cohorts, tissues, and measurement platforms. Here, we address this problem by formulating molecular system inference as a multi-dataset integration task and by applying the Matthews Correlation Coefficient-Recursive Ensemble Feature Selection (MCC-REFS) algorithm to jointly analyze five independent transcriptomic datasets spanning peripheral blood mononuclear cells, whole blood, plasma, and post-mortem tissues. We compared MCC-REFS with three commonly used feature-selection strategies, GRACES, SelectKBest, and Deep Neural Pursuit (DNP), in order to evaluate robustness, convergence, and cross-context reproducibility. MCC-REFS consistently converged on a compact seven-gene system (
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