{"entity": "researcher", "timestamp": "2026-08-20T21:38:57.814Z", "family": "Celli", "given": "Ludovica", "initials": "L", "orcid": "0000-0002-5847-5856", "affiliations": ["Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden.", "Institute of Molecular Genetics Luigi Luca Cavalli-Sforza, National Research Council, 27100 Pavia, Italy.", "Department of Biology and Biotechnology, University of Pavia, 27100 Pavia, Italy."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/914adbe626ea431ab9a30b5b51f3c7b0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/914adbe626ea431ab9a30b5b51f3c7b0"}}, "publications": [{"entity": "publication", "iuid": "3f210f528f12413ba181421bed6f8130", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/3f210f528f12413ba181421bed6f8130.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/3f210f528f12413ba181421bed6f8130"}}, "title": "Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment.", "authors": [{"family": "Garbulowski", "given": "Mateusz", "initials": "M", "orcid": "0000-0002-2497-194X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/98de355d5a1247cebb494b16444857e6.json"}}, {"family": "Smolinska", "given": "Karolina", "initials": "K", "orcid": "0000-0003-0907-5298", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b491565d0ea04ceb897ce53f325781b3.json"}}, {"family": "\u00c7abuk", "given": "U\u011fur", "initials": "U", "orcid": "0000-0002-4443-3065", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f8ece88ac79e4f4c83714b6b83bcf90c.json"}}, {"family": "Yones", "given": "Sara A", "initials": "SA", "orcid": "0000-0002-7201-2604", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a1db6ae5db964f149ffc93ea78561711.json"}}, {"family": "Celli", "given": "Ludovica", "initials": "L", "orcid": "0000-0002-5847-5856", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/914adbe626ea431ab9a30b5b51f3c7b0.json"}}, {"family": "Yaz", "given": "Esma Nur", "initials": "EN"}, {"family": "Barren\u00e4s", "given": "Fredrik", "initials": "F"}, {"family": "Diamanti", "given": "Klev", "initials": "K", "orcid": "0000-0002-4922-8415", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8638ca04ea7f4ff8a191f3c6969b1394.json"}}, {"family": "Wadelius", "given": "Claes", "initials": "C"}, {"family": "Komorowski", "given": "Jan", "initials": "J", "orcid": "0000-0002-0766-8789", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1dffb9dd373741069230b4f80d7e1698.json"}}], "type": "journal article", "published": "2022-02-17", "journal": {"title": "Cancers (Basel)", "issn": "2072-6694", "volume": "14", "issue": "4", "issn-l": "2072-6694"}, "abstract": "Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.", "doi": "10.3390/cancers14041014", "pmid": "35205761", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8870250"}, {"db": "pii", "key": "cancers14041014"}], "notes": [], "created": "2026-08-20T13:39:39.410Z", "modified": "2026-08-20T13:39:39.667Z"}]}