{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:12:34Z","timestamp":1788415954886,"version":"build-2803163510"},"reference-count":75,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Syst. Biol."],"abstract":"<jats:p>\n                    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\u2013Recursive 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 (\n                    <jats:italic>PPP2CB<\/jats:italic>\n                    ,\n                    <jats:italic>SOCS3<\/jats:italic>\n                    ,\n                    <jats:italic>ARG1<\/jats:italic>\n                    ,\n                    <jats:italic>IL6R<\/jats:italic>\n                    ,\n                    <jats:italic>ECHS1<\/jats:italic>\n                    ,\n                    <jats:italic>FZD2<\/jats:italic>\n                    ,\n                    <jats:italic>TRGV3\/5<\/jats:italic>\n                    ) exhibiting higher stability indices and stronger classification performance than alternative methods. Generalization was assessed using an independent multi-layer perceptron classifier across validation cohorts with differing tissue origin and sequencing technologies, demonstrating preservation of discriminative structure. To support interpretation, we integrated functional, pharmacological, and interventional knowledge from\n                    <jats:italic>DrugBank<\/jats:italic>\n                    ,\n                    <jats:italic>DGIdb<\/jats:italic>\n                    , and\n                    <jats:italic>Open Targets<\/jats:italic>\n                    , enabling the mapping of inferred gene systems onto pathways, known drug targets, and ongoing clinical investigations. Taken together, this work presents an algorithmic framework for multi-dataset and multi-omics integration in systems microbiology, illustrating how stable and interpretable molecular patterns can be identified from heterogeneous data, with Long COVID serving as a representative case study.\n                  <\/jats:p>","DOI":"10.3389\/fsysb.2026.1873899","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:14:55Z","timestamp":1788412495000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A systems microbiology framework for reproducible multi-dataset omics integration with application to long COVID"],"prefix":"10.3389","volume":"6","author":[{"given":"Brigitta","family":"Varga","sequence":"first","affiliation":[{"name":"Informatics Institute, University of Amsterdam","place":["Amsterdam, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marlet","family":"Martinez-Archundia","sequence":"additional","affiliation":[{"name":"Laboratory for the Design and Development of New Drugs and Biotechnological Innovation, Higher School of Medicine, National Polytechnic Institute (IPN)","place":["Mexico, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linette E. M.","family":"Willemsen","sequence":"additional","affiliation":[{"name":"Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan","family":"Garssen","sequence":"additional","affiliation":[{"name":"Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht","place":["Utrecht, Netherlands"]},{"name":"Global Centre of Excellence Immunology, Danone Nutricia Research","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandro","family":"Lopez-Rincon","sequence":"additional","affiliation":[{"name":"Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1093\/bioinformatics\/btp630","article-title":"Robust biomarker identification for cancer diagnosis with ensemble feature selection methods","volume":"26","author":"Abeel","year":"2010","journal-title":"Bioinformatics"},{"key":"B2","unstructured":"Persistent activation of chronic inflammatory pathways in long covid\n          \n          \n            \n              Aid\n              M.\n            \n            \n              Barouch\n              D. 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