{"entity": "researcher", "timestamp": "2026-08-17T02:00:50.199Z", "family": "Yan", "given": "Yingxiao", "initials": "Y", "orcid": "0000-0002-9196-7326", "affiliations": ["Food and Nutrition Sciences, Department of Life Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden."], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/4987cb247e0741298ab66cd95acdc1b5.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/4987cb247e0741298ab66cd95acdc1b5"}}, "publications": [{"entity": "publication", "iuid": "8aed0fdd898340b0abe5b90b8216abfb", "links": {"self": {"href": "https://publications.scilifelab.se/publication/8aed0fdd898340b0abe5b90b8216abfb.json"}, "display": {"href": "https://publications.scilifelab.se/publication/8aed0fdd898340b0abe5b90b8216abfb"}}, "title": "Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2.", "authors": [{"family": "Yan", "given": "Yingxiao", "initials": "Y", "orcid": "0000-0002-9196-7326", "researcher": {"href": "https://publications.scilifelab.se/researcher/4987cb247e0741298ab66cd95acdc1b5.json"}}, {"family": "Schillemans", "given": "Tessa", "initials": "T"}, {"family": "Skantze", "given": "Viktor", "initials": "V", "orcid": "0000-0003-1350-1165", "researcher": {"href": "https://publications.scilifelab.se/researcher/d31ba31c4aef4e75be2bf3d6b245ec73.json"}}, {"family": "Brunius", "given": "Carl", "initials": "C", "orcid": "0000-0003-3957-870X", "researcher": {"href": "https://publications.scilifelab.se/researcher/560a5d14ee83421680058b00df2ac9e2.json"}}], "type": "journal article", "published": "2024-04-04", "journal": {"title": "Bioinform Adv", "issn": "2635-0041", "volume": "4", "issue": "1", "pages": "vbae051", "issn-l": null}, "abstract": "Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and for example exposures and health conditions. ML is also used for feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework.\n\nThe MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.\n\nAlgorithms, data, scripts, and a tutorial are open source under GPL-3 license and available in the MUVR2 R package at https://github.com/MetaboComp/MUVR2.", "doi": "10.1093/bioadv/vbae051", "pmid": "38645717", "labels": {"Chalmers Mass Spectrometry Infrastructure": "Technology development"}, "xrefs": [{"db": "pmc", "key": "PMC11031361"}, {"db": "pii", "key": "vbae051"}], "notes": [], "created": "2024-11-27T15:32:55.956Z", "modified": "2024-11-27T15:32:56.018Z"}, {"entity": "publication", "iuid": "6ca1a5c90b4c4045b65788781937d88a", "links": {"self": {"href": "https://publications.scilifelab.se/publication/6ca1a5c90b4c4045b65788781937d88a.json"}, "display": {"href": "https://publications.scilifelab.se/publication/6ca1a5c90b4c4045b65788781937d88a"}}, "title": "OMICs Signatures Linking Persistent Organic Pollutants to Cardiovascular Disease in the Swedish Mammography Cohort.", "authors": [{"family": "Schillemans", "given": "Tessa", "initials": "T", "orcid": "0000-0002-1044-1630", "researcher": {"href": "https://publications.scilifelab.se/researcher/740a568c06564e148286fcd40a094341.json"}}, {"family": "Yan", "given": "Yingxiao", "initials": "Y", "orcid": "0000-0002-9196-7326", "researcher": {"href": "https://publications.scilifelab.se/researcher/4987cb247e0741298ab66cd95acdc1b5.json"}}, {"family": "Ribbenstedt", "given": "Anton", "initials": "A"}, {"family": "Donat-Vargas", "given": "Carolina", "initials": "C"}, {"family": "Lindh", "given": "Christian H", "initials": "CH"}, {"family": "Kiviranta", "given": "Hannu", "initials": "H"}, {"family": "Rantakokko", "given": "Panu", "initials": "P"}, {"family": "Wolk", "given": "Alicja", "initials": "A"}, {"family": "Landberg", "given": "Rikard", "initials": "R"}, {"family": "\u00c5kesson", "given": "Agneta", "initials": "A"}, {"family": "Brunius", "given": "Carl", "initials": "C"}], "type": "journal article", "published": "2024-01-16", "journal": {"title": "Environ. Sci. Technol.", "issn": "1520-5851", "volume": "58", "issue": "2", "pages": "1036-1047", "issn-l": "0013-936X"}, "abstract": "Cardiovascular disease (CVD) development may be linked to persistent organic pollutants (POPs), including organochlorine compounds (OCs) and perfluoroalkyl and polyfluoroalkyl substances (PFAS). To explore underlying mechanisms, we investigated metabolites, proteins, and genes linking POPs with CVD risk. We used data from a nested case-control study on myocardial infarction (MI) and stroke from the Swedish Mammography Cohort - Clinical (n = 657 subjects). OCs, PFAS, and multiomics (9511 liquid chromatography-mass spectrometry (LC-MS) metabolite features; 248 proteins; 8110 gene variants) were measured in baseline plasma. POP-related omics features were selected using random forest followed by Spearman correlation adjusted for confounders. From these, CVD-related omics features were selected using conditional logistic regression. Finally, 29 (for OCs) and 12 (for PFAS) unique features associated with POPs and CVD. One omics subpattern, driven by lipids and inflammatory proteins, associated with MI (OR = 2.03; 95% CI = 1.47; 2.79), OCs, age, and BMI, and correlated negatively with PFAS. Another subpattern, driven by carnitines, associated with stroke (OR = 1.55; 95% CI = 1.16; 2.09), OCs, and age, but not with PFAS. This may imply that OCs and PFAS associate with different omics patterns with opposite effects on CVD risk, but more research is needed to disentangle potential modifications by other factors.", "doi": "10.1021/acs.est.3c06388", "pmid": "38174696", "labels": {"Chalmers Mass Spectrometry Infrastructure": "Collaborative", "Affinity Proteomics Uppsala": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC10795192"}], "notes": [], "created": "2024-11-27T15:31:37.796Z", "modified": "2025-11-27T11:25:58.353Z"}]}