{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:47:54Z","timestamp":1787028474364,"version":"build-2736575974"},"reference-count":96,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Governmental Research Funding","award":["TYH2014214"],"award-info":[{"award-number":["TYH2014214"]}]},{"name":"Landesoffensive zur Entwicklung wissenschaftlich\u2212\u00f6konomischer Exzellenz (LOEWE), Zentrum: Translational Medicine and Pharmacology (JL)","award":["TYH2014214"],"award-info":[{"award-number":["TYH2014214"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.<\/jats:p>","DOI":"10.3390\/ijms23095085","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T08:26:35Z","timestamp":1651566395000},"page":"5085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3504-4663","authenticated-orcid":false,"given":"Teemu","family":"Miettinen","sequence":"first","affiliation":[{"name":"Pain Clinic, Department of Perioperative Medicine, Intensive Care and Pain Medicine, Helsinki University Hospital and SleepWell Research Programme, University of Helsinki, 00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8999-6040","authenticated-orcid":false,"given":"Anni I.","family":"Nieminen","sequence":"additional","affiliation":[{"name":"Metabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pekka","family":"M\u00e4ntyselk\u00e4","sequence":"additional","affiliation":[{"name":"Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Finland, and Primary Health Care Unit, Kuopio University Hospital, 70211 Kuopio, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4899-605X","authenticated-orcid":false,"given":"Eija","family":"Kalso","sequence":"additional","affiliation":[{"name":"Pain Clinic, Department of Perioperative Medicine, Intensive Care and Pain Medicine, Helsinki University Hospital and SleepWell Research Programme, University of Helsinki, 00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5818-6958","authenticated-orcid":false,"given":"J\u00f6rn","family":"L\u00f6tsch","sequence":"additional","affiliation":[{"name":"Institute of Clinical Pharmacology, Goethe\u2014University, Theodor\u2014Stern\u2014Kai 7, 60590 Frankfurt am Main, Germany"},{"name":"Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1152\/physrev.00035.2018","article-title":"Metabolomics for Investigating Physiological and Pathophysiological Processes","volume":"99","author":"Wishart","year":"2019","journal-title":"Physiol. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.joca.2011.10.010","article-title":"Global metabolic profiling of human osteoarthritic synovium","volume":"20","author":"Adams","year":"2012","journal-title":"Osteoarthr. Cartil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jpba.2016.02.004","article-title":"Exploration of the serum metabolite signature in patients with rheumatoid arthritis using gas chromatography-mass spectrometry","volume":"127","author":"Zhou","year":"2016","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1021\/pr5007729","article-title":"Urinary metabolite profiling combined with computational analysis predicts interstitial cystitis-associated candidate biomarkers","volume":"14","author":"Wen","year":"2015","journal-title":"J. Proteome Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.ebiom.2019.07.031","article-title":"Gut microbiome and serum metabolome analyses identify molecular biomarkers and altered glutamate metabolism in fibromyalgia","volume":"46","author":"Abecia","year":"2019","journal-title":"EBioMedicine"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Malatji, B.G., Meyer, H., Mason, S., Engelke, U.F.H., Wevers, R.A., van Reenen, M., and Reinecke, C.J. (2017). A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls. BMC Neurol., 17.","DOI":"10.1186\/s12883-017-0863-9"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1111\/cts.12679","article-title":"Metabolomic Differentials in Women With and Without Fibromyalgia","volume":"13","author":"Menzies","year":"2020","journal-title":"Clin. Transl. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Finco, G., Locci, E., Mura, P., Massa, R., Noto, A., Musu, M., Landoni, G., d\u2019Aloja, E., De-Giorgio, F., and Scano, P. (2016). Can Urine Metabolomics Be Helpful in Differentiating Neuropathic and Nociceptive Pain? A Proof-of-Concept Study. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0150476"},{"key":"ref_9","first-page":"1145","article-title":"The Association between Musculoskeletal Pain and Circulating Ornithine: A Population-Based Study","volume":"18","author":"Mantyselka","year":"2017","journal-title":"Pain Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15925","DOI":"10.1038\/srep15925","article-title":"Systemic differences in serum metabolome: A cross sectional comparison of women with localised and widespread pain and controls","volume":"5","author":"Hadrevi","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1097\/j.pain.0000000000000200","article-title":"An omics investigation into chronic widespread musculoskeletal pain reveals epiandrosterone sulfate as a potential biomarker","volume":"156","author":"Livshits","year":"2015","journal-title":"Pain"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1002\/ejp.1677","article-title":"Metabolomics in chronic pain research","volume":"25","author":"Teckchandani","year":"2021","journal-title":"Eur. J. Pain"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1097\/j.pain.0000000000002002","article-title":"Machine learning suggests sleep as a core factor in chronic pain","volume":"162","author":"Miettinen","year":"2021","journal-title":"Pain"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jpsychores.2009.05.001","article-title":"The epidemiology of insomnia: Associations with physical and mental health: The HUNT-2 study","volume":"67","author":"Sivertsen","year":"2009","journal-title":"J. Psychosom. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1016\/j.jpain.2013.08.007","article-title":"The association of sleep and pain: An update and a path forward","volume":"14","author":"Finan","year":"2013","journal-title":"J. Pain Off. J. Am. Pain Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1093\/pm\/pnz343","article-title":"Chronic Pain and Sleep Disturbances: A Pragmatic Review of Their Relationships, Comorbidities, and Treatments","volume":"21","author":"Husak","year":"2020","journal-title":"Pain Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Humer, E., Pieh, C., and Brandmayr, G. (2020). Metabolomics in Sleep, Insomnia and Sleep Apnea. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21197244"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Miettinen, T., Kautiainen, H., M\u00e4ntyselk\u00e4, P., Linton, S.J., and Kalso, E. (2019). Pain interference type and level guide the assessment process in chronic pain: Categorizing pain patients entering tertiary pain treatment with the Brief Pain Inventory. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0221437"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11306-019-1553-y","article-title":"Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies","volume":"15","author":"Gil","year":"2019","journal-title":"Metabolomics"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nandania, J., Peddinti, G., Pessia, A., Kokkonen, M., and Velagapudi, V. (2018). Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry. Metabolites, 8.","DOI":"10.1101\/352468"},{"key":"ref_21","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, The MIT Press."},{"key":"ref_22","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","unstructured":"R Development Core Team (2022, March 01). R: A Language and Environment for Statistical Computing. Available online: https:\/\/CRAN.R-project.org\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.18637\/jss.v036.i11","article-title":"Feature Selection with the Boruta Package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis, Springer.","DOI":"10.1007\/978-0-387-98141-3"},{"key":"ref_26","unstructured":"Kuhn, M. (2022, March 01). Caret: Classification and Regression Training. Available online: https:\/\/cran.r-project.org\/package=caret."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., and M\u00fcller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-77"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"W388","DOI":"10.1093\/nar\/gkab382","article-title":"MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights","volume":"49","author":"Pang","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"399","DOI":"10.2147\/JPR.S55598","article-title":"The association between chronic pain and obesity","volume":"8","author":"Okifuji","year":"2015","journal-title":"J. Pain Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.3389\/fimmu.2019.03009","article-title":"Cytokines in Pain: Harnessing Endogenous Anti-Inflammatory Signaling for Improved Pain Management","volume":"10","author":"Vanderwall","year":"2019","journal-title":"Front. Immunol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.cmet.2009.02.002","article-title":"A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance","volume":"9","author":"Newgard","year":"2009","journal-title":"Cell Metab."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rockel, J.S., and Kapoor, M. (2018). The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology. Metabolites, 8.","DOI":"10.3390\/metabo8040092"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.freeradbiomed.2017.01.009","article-title":"High concentration of branched-chain amino acids promotes oxidative stress, inflammation and migration of human peripheral blood mononuclear cells via mTORC1 activation","volume":"104","author":"Zhenyukh","year":"2017","journal-title":"Free Radic. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.bbi.2013.01.005","article-title":"Glucuronic acid and the ethanol metabolite ethyl-glucuronide cause toll-like receptor 4 activation and enhanced pain","volume":"30","author":"Lewis","year":"2013","journal-title":"Brain Behav. Immun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.exger.2004.02.007","article-title":"Effects of paradoxical sleep deprivation on blood parameters associated with cardiovascular risk in aged rats","volume":"39","author":"Andersen","year":"2004","journal-title":"Exp. Gerontol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Trivedi, M.S., Holger, D., Bui, A.T., Craddock, T.J.A., and Tartar, J.L. (2017). Short-term sleep deprivation leads to decreased systemic redox metabolites and altered epigenetic status. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181978"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kervezee, L., Cermakian, N., and Boivin, D.B. (2019). Individual metabolomic signatures of circadian misalignment during simulated night shifts in humans. PLoS Biol., 17.","DOI":"10.1371\/journal.pbio.3000303"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1056\/NEJMoa011613","article-title":"Plasma homocysteine as a risk factor for dementia and Alzheimer\u2019s disease","volume":"346","author":"Seshadri","year":"2002","journal-title":"N. Engl. J. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.tcm.2020.04.003","article-title":"Advances and challenges in pursuing biomarkers for obstructive sleep apnea: Implications for the cardiovascular risk","volume":"31","author":"Lebkuchen","year":"2021","journal-title":"Trends Cardiovasc. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5664\/jcsm.7588","article-title":"Short Sleep Duration Is Associated with Increased Serum Homocysteine: Insights from a National Survey","volume":"15","author":"Chen","year":"2019","journal-title":"J. Clin. Sleep Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s10194-015-0540-6","article-title":"Sleep apnoea headache in obstructive sleep apnoea syndrome patients presenting with morning headache: Comparison of the ICHD-2 and ICHD-3 beta criteria","volume":"16","author":"Suzuki","year":"2015","journal-title":"J. Headache Pain"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"200220","DOI":"10.1183\/16000617.0220-2020","article-title":"Metabolomics and microbiome profiling as biomarkers in obstructive sleep apnoea: A comprehensive review","volume":"30","author":"Zhang","year":"2021","journal-title":"Eur. Respir. Rev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2538S","DOI":"10.1093\/jn\/nxaa254","article-title":"Metabolic Consequences of Supplemented Methionine in a Clinical Context","volume":"150","author":"Engelen","year":"2020","journal-title":"J. Nutr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1742-4682-5-8","article-title":"A mathematical model of glutathione metabolism","volume":"5","author":"Reed","year":"2008","journal-title":"Biol. Med. Model."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1093\/jn\/134.3.489","article-title":"Glutathione metabolism and its implications for health","volume":"134","author":"Wu","year":"2004","journal-title":"J. Nutr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s12929-019-0527-8","article-title":"Implications of altered NAD metabolism in metabolic disorders","volume":"26","author":"Okabe","year":"2019","journal-title":"J. Biomed. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/s41392-020-00311-7","article-title":"NAD(+) metabolism: Pathophysiologic mechanisms and therapeutic potential","volume":"5","author":"Xie","year":"2020","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"587","DOI":"10.14348\/molcells.2015.0126","article-title":"A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes","volume":"38","author":"Park","year":"2015","journal-title":"Mol. Cells"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.molmed.2010.12.006","article-title":"Pain-relieving prospects for adenosine receptors and ectonucleotidases","volume":"17","author":"Zylka","year":"2011","journal-title":"Trends Mol. Med."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.celrep.2016.05.080","article-title":"Sustained Elevated Adenosine via ADORA2B Promotes Chronic Pain through Neuro-immune Interaction","volume":"16","author":"Hu","year":"2016","journal-title":"Cell Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"13892","DOI":"10.1073\/pnas.1518285112","article-title":"Why significant variables aren\u2019t automatically good predictors","volume":"112","author":"Lo","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Imaizumi, T., Okada, A., Miyamoto, S., Sakaori, F., Yamamoto, Y., and Vichi, M. (2020). Random Forests Followed by Computed ABC Analysis as a Feature Selection Method for Machine Learning in Biomedical Data. Advanced Studies in Classification and Data Science, Springer.","DOI":"10.1007\/978-981-15-3311-2"},{"key":"ref_53","first-page":"1","article-title":"Feature selection using lasso","volume":"30","author":"Fonti","year":"2017","journal-title":"VU Amst. Res. Pap. Bus. Anal."},{"key":"ref_54","first-page":"129","article-title":"Pain assessment: Global use of the Brief Pain Inventory","volume":"23","author":"Cleeland","year":"1994","journal-title":"Ann. Acad. Med. Singap."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1002\/ejp.1683","article-title":"Interpretation of cluster structures in pain-related phenotype data using explainable artificial intelligence (XAI)","volume":"25","author":"Malkusch","year":"2021","journal-title":"Eur. J. Pain"},{"key":"ref_56","first-page":"1","article-title":"Sleep problems in pain patients entering tertiary pain care: The role of pain-related anxiety, medication use, self-reported diseases, and sleep disorders","volume":"00","author":"Miettinen","year":"2021","journal-title":"Pain"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"328","DOI":"10.3109\/07853890109002086","article-title":"The 15D instrument of health-related quality of life: Properties and applications","volume":"33","author":"Sintonen","year":"2001","journal-title":"Ann. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1111\/j.1365-2869.1995.tb00205.x","article-title":"Basic Nordic Sleep Questionnaire (BNSQ): A quantitated measure of subjective sleep complaints","volume":"4","author":"Partinen","year":"1995","journal-title":"J. Sleep Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"14884","DOI":"10.1038\/s41598-018-33077-8","article-title":"Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy","volume":"8","author":"Schiffmann","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biometrics"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1214\/aoms\/1177730491","article-title":"On a test of whether one of two random variables is stochastically larger than the other","volume":"18","author":"Mann","year":"1947","journal-title":"Ann. Math. Stat."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/14786440009463897","article-title":"On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling","volume":"50","author":"Pearson","year":"1900","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_63","first-page":"3","article-title":"Teoria statistica delle classi e calcolo delle probabilita","volume":"8","author":"Bonferroni","year":"1936","journal-title":"Pubbl. Del R Ist. Super. Di Sci. Econ. E Commer. Di Firenze"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/10618600.1996.10474713","article-title":"R: A Language for Data Analysis and Graphics","volume":"5","author":"Ihaka","year":"1996","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"43946","DOI":"10.1038\/srep43946","article-title":"Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies","volume":"7","author":"Ciucci","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1037\/h0070888","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_68","unstructured":"Ho, T.K. (1995, January 14\u201316). Random decision forests. Proceedings of the Third International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","article-title":"MissForest--non-parametric missing value imputation for mixed-type data","volume":"28","author":"Stekhoven","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_71","unstructured":"Good, P.I. (2006). Resampling Methods: A Practical Guide to Data Analysis, Birkh\u00e4user."},{"key":"ref_72","unstructured":"Till\u00e9, Y., and Matei, A. (2022, March 01). Sampling: Survey Sampling. Available online: https:\/\/cran.r\u2212project.org\/package=sampling."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., and Buhmann, J.M. (2010, January 23\u201326). The Balanced Accuracy and Its Posterior Distribution. Proceedings of the 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.764"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TIT.1954.1057460","article-title":"The theory of signal detectability","volume":"4","author":"Peterson","year":"1954","journal-title":"Trans. IRE Prof. Group Inf. Theory"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1136\/bmj.309.6947.102","article-title":"Diagnostic tests 2: Predictive values","volume":"309","author":"Altman","year":"1994","journal-title":"BMJ"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1136\/bmj.308.6943.1552","article-title":"Diagnostic tests. 1: Sensitivity and specificity","volume":"308","author":"Altman","year":"1994","journal-title":"BMJ"},{"key":"ref_77","first-page":"1","article-title":"A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons","volume":"5","year":"1948","journal-title":"Kongelige Danske Videnskabernes Selskab, Biologiske Skrifter"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/0020-0271(71)90051-9","article-title":"The use of hierarchic clustering in information retrieval","volume":"7","author":"Jardine","year":"1971","journal-title":"Inf. Storage Retr."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1097\/j.pain.0000000000001118","article-title":"Machine learning in pain research","volume":"159","author":"Lotsch","year":"2017","journal-title":"Pain"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_81","first-page":"771","article-title":"A short introduction to boosting","volume":"14","author":"Schapire","year":"1999","journal-title":"J. Jpn. Soc. Artif. Intell."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theor."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1198\/106186006X133933","article-title":"Unbiased Recursive Partitioning: A Conditional Inference Framework","volume":"15","author":"Hothorn","year":"2006","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_84","unstructured":"Breimann, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1993). Classification and Regression Trees, Chapman and Hall."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of Decision Trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_86","unstructured":"Frank, E., and Witten, I.H. (1998, January 24\u201327). Generating Accurate Rule Sets Without Global Optimization. Proceedings of the ICML, Madison, WI, USA."},{"key":"ref_87","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"kernlab\u2014An S4 Package for Kernel Methods in R","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw."},{"key":"ref_89","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., and Zhou, T. (2022, March 01). xgboost: Extreme Gradient Boosting. Available online: https:\/\/cran.r\u2212project.org\/package=xgboost."},{"key":"ref_90","unstructured":"Therneau, T., and Atkinson, B. (2022, March 01). rpart: Recursive Partitioning and Regression Trees. Available online: https:\/\/cran.r\u2212project.org\/package=rpart."},{"key":"ref_91","unstructured":"Kuhn, M., and Quinlan, R. (2022, March 01). C50: C5.0 Decision Trees and Rule-Based Models. Available online: https:\/\/CRAN.R\u2212project.org\/package=C50."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00180-008-0119-7","article-title":"Open-source machine learning: R meets Weka","volume":"24","author":"Hornik","year":"2009","journal-title":"Comput. Stat."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"W652","DOI":"10.1093\/nar\/gkp356","article-title":"MetaboAnalyst: A web server for metabolomic data analysis and interpretation","volume":"37","author":"Xia","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1093\/bioinformatics\/btm069","article-title":"pcaMethods\u2014A bioconductor package providing PCA methods for incomplete data","volume":"23","author":"Stacklies","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"W71","DOI":"10.1093\/nar\/gkq329","article-title":"MSEA: A web-based tool to identify biologically meaningful patterns in quantitative metabolomic data","volume":"38","author":"Xia","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/23\/9\/5085\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:45Z","timestamp":1760137545000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/23\/9\/5085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,3]]},"references-count":96,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["ijms23095085"],"URL":"https:\/\/doi.org\/10.3390\/ijms23095085","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,3]]}}}