{"entity": "researcher", "timestamp": "2026-08-20T21:48:49.435Z", "family": "Smolinska", "given": "Karolina", "initials": "K", "orcid": "0000-0003-0907-5298", "affiliations": ["Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/b491565d0ea04ceb897ce53f325781b3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/b491565d0ea04ceb897ce53f325781b3"}}, "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"}, {"entity": "publication", "iuid": "124441b2d26448a49a238d854fcc0d5c", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/124441b2d26448a49a238d854fcc0d5c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/124441b2d26448a49a238d854fcc0d5c"}}, "title": "Functional annotation of noncoding mutations in cancer.", "authors": [{"family": "Umer", "given": "Husen M", "initials": "HM", "orcid": "0000-0003-3971-2462", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/922625c410444833a263244d0313980e.json"}}, {"family": "Smolinska", "given": "Karolina", "initials": "K", "orcid": "0000-0003-0907-5298", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b491565d0ea04ceb897ce53f325781b3.json"}}, {"family": "Komorowski", "given": "Jan", "initials": "J", "orcid": "0000-0003-0907-5298", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b491565d0ea04ceb897ce53f325781b3.json"}}, {"family": "Wadelius", "given": "Claes", "initials": "C", "orcid": "0000-0002-2033-7829", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f01aa16d2b3343e980a2f2a9eb93af91.json"}}], "type": "journal article", "published": "2021-09-00", "journal": {"title": "Life Sci. Alliance", "issn": "2575-1077", "volume": "4", "issue": "9", "issn-l": null}, "abstract": "In a cancer genome, the noncoding sequence contains the vast majority of somatic mutations. While very few are expected to be cancer drivers, those affecting regulatory elements have the potential to have downstream effects on gene regulation that may contribute to cancer progression. To prioritize regulatory mutations, we screened somatic mutations in the Pan-Cancer Analysis of Whole Genomes cohort of 2,515 cancer genomes on individual bases to assess their potential regulatory roles in their respective cancer types. We found a highly significant enrichment of regulatory mutations associated with the deamination signature overlapping a CpG site in the CCAAT/Enhancer Binding Protein \u03b2 recognition sites in many cancer types. Overall, 5,749 mutated regulatory elements were identified in 1,844 tumor samples from 39 cohorts containing 11,962 candidate regulatory mutations. Our analysis indicated 20 or more regulatory mutations in 5.5% of the samples, and an overall average of six per tumor. Several recurrent elements were identified, and major cancer-related pathways were significantly enriched for genes nearby the mutated regulatory elements. Our results provide a detailed view of the role of regulatory elements in cancer genomes.", "doi": "10.26508/lsa.201900523", "pmid": "34282050", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8321657"}, {"db": "pii", "key": "4/9/e201900523"}], "notes": [], "created": "2026-08-20T13:03:53.335Z", "modified": "2026-08-20T13:03:53.475Z"}, {"entity": "publication", "iuid": "124441b2d26448a49a238d854fcc0d5c", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/124441b2d26448a49a238d854fcc0d5c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/124441b2d26448a49a238d854fcc0d5c"}}, "title": "Functional annotation of noncoding mutations in cancer.", "authors": [{"family": "Umer", "given": "Husen M", "initials": "HM", "orcid": "0000-0003-3971-2462", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/922625c410444833a263244d0313980e.json"}}, {"family": "Smolinska", "given": "Karolina", "initials": "K", "orcid": "0000-0003-0907-5298", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b491565d0ea04ceb897ce53f325781b3.json"}}, {"family": "Komorowski", "given": "Jan", "initials": "J", "orcid": "0000-0003-0907-5298", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b491565d0ea04ceb897ce53f325781b3.json"}}, {"family": "Wadelius", "given": "Claes", "initials": "C", "orcid": "0000-0002-2033-7829", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f01aa16d2b3343e980a2f2a9eb93af91.json"}}], "type": "journal article", "published": "2021-09-00", "journal": {"title": "Life Sci. Alliance", "issn": "2575-1077", "volume": "4", "issue": "9", "issn-l": null}, "abstract": "In a cancer genome, the noncoding sequence contains the vast majority of somatic mutations. While very few are expected to be cancer drivers, those affecting regulatory elements have the potential to have downstream effects on gene regulation that may contribute to cancer progression. To prioritize regulatory mutations, we screened somatic mutations in the Pan-Cancer Analysis of Whole Genomes cohort of 2,515 cancer genomes on individual bases to assess their potential regulatory roles in their respective cancer types. We found a highly significant enrichment of regulatory mutations associated with the deamination signature overlapping a CpG site in the CCAAT/Enhancer Binding Protein \u03b2 recognition sites in many cancer types. Overall, 5,749 mutated regulatory elements were identified in 1,844 tumor samples from 39 cohorts containing 11,962 candidate regulatory mutations. Our analysis indicated 20 or more regulatory mutations in 5.5% of the samples, and an overall average of six per tumor. Several recurrent elements were identified, and major cancer-related pathways were significantly enriched for genes nearby the mutated regulatory elements. Our results provide a detailed view of the role of regulatory elements in cancer genomes.", "doi": "10.26508/lsa.201900523", "pmid": "34282050", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8321657"}, {"db": "pii", "key": "4/9/e201900523"}], "notes": [], "created": "2026-08-20T13:03:53.335Z", "modified": "2026-08-20T13:03:53.475Z"}]}