{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:17:05Z","timestamp":1785197825805,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Diabetic nephropathy, hypertension, and glomerulonephritis are the most common causes of chronic kidney diseases (CKD). Since CKD of various origins may not become apparent until kidney function is significantly impaired, a differential diagnosis and an appropriate treatment are needed at the very early stages. Conventional biomarkers may not have sufficient separation capabilities, while a full-proteomic approach may be used for these purposes. In the current study, several machine learning algorithms were examined for the differential diagnosis of CKD of three origins. The tested dataset was based on whole proteomic data obtained after the mass spectrometric analysis of plasma and urine samples of 34 CKD patients and the use of label-free quantification approach. The k-nearest-neighbors algorithm showed the possibility of separation of a healthy group from renal patients in general by proteomics data of plasma with high confidence (97.8%). This algorithm has also be proven to be the best of the three tested for distinguishing the groups of patients with diabetic nephropathy and glomerulonephritis according to proteomics data of plasma (96.3% of correct decisions). The group of hypertensive nephropathy could not be reliably separated according to plasma data, whereas analysis of entire proteomics data of urine did not allow differentiating the three diseases. Nevertheless, the group of hypertensive nephropathy was reliably separated from all other renal patients using the k-nearest-neighbors classifier \u201cone against all\u201d with 100% of accuracy by urine proteome data. The tested algorithms show good abilities to differentiate the various groups across proteomic data sets, which may help to avoid invasive intervention for the verification of the glomerulonephritis subtypes, as well as to differentiate hypertensive and diabetic nephropathy in the early stages based not on individual biomarkers, but on the whole proteomic composition of urine and blood.<\/jats:p>","DOI":"10.3390\/ijms21134802","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T10:41:09Z","timestamp":1594118469000},"page":"4802","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Proteomics-Based Machine Learning Approach as an Alternative to Conventional Biomarkers for Differential Diagnosis of Chronic Kidney Diseases"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2826-5751","authenticated-orcid":false,"given":"Yury E.","family":"Glazyrin","sequence":"first","affiliation":[{"name":"Laboratory for Biomolecular and Medical Technologies, Krasnoyarsk State Medical University Named after Prof. V.F. Voyno-Yasenetsky, 660022 Krasnoyarsk, Russia"},{"name":"Laboratory for Digital Controlled Drugs and Theranostics, Federal Research Center \u201cKrasnoyarsk Science Center of the Siberian Branch of the Russian Academy of Science\u201d, 660036 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitry V.","family":"Veprintsev","sequence":"additional","affiliation":[{"name":"Laboratory for Digital Controlled Drugs and Theranostics, Federal Research Center \u201cKrasnoyarsk Science Center of the Siberian Branch of the Russian Academy of Science\u201d, 660036 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irina A.","family":"Ler","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Krasnoyarsk Interdistrict Clinical Hospital of Emergency Medical Care Named after N.S. Karpovich, 660062 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria L.","family":"Rossovskaya","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Krasnoyarsk Interdistrict Clinical Hospital of Emergency Medical Care Named after N.S. Karpovich, 660062 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana A.","family":"Varygina","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Krasnoyarsk Interdistrict Clinical Hospital of Emergency Medical Care Named after N.S. Karpovich, 660062 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia L.","family":"Glizer","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Krasnoyarsk Interdistrict Clinical Hospital of Emergency Medical Care Named after N.S. Karpovich, 660062 Krasnoyarsk, Russia"},{"name":"Faculty of Medicine, Krasnoyarsk State Medical University Named after Prof. V.F. Voyno-Yasenetsky, 660022 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatiana N.","family":"Zamay","sequence":"additional","affiliation":[{"name":"Laboratory for Biomolecular and Medical Technologies, Krasnoyarsk State Medical University Named after Prof. V.F. Voyno-Yasenetsky, 660022 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marina M.","family":"Petrova","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Krasnoyarsk State Medical University Named after Prof. V.F. Voyno-Yasenetsky, 660022 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoran","family":"Minic","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON K1N6N5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0514-599X","authenticated-orcid":false,"given":"Maxim V.","family":"Berezovski","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON K1N6N5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna S.","family":"Kichkailo","sequence":"additional","affiliation":[{"name":"Laboratory for Biomolecular and Medical Technologies, Krasnoyarsk State Medical University Named after Prof. V.F. Voyno-Yasenetsky, 660022 Krasnoyarsk, Russia"},{"name":"Laboratory for Digital Controlled Drugs and Theranostics, Federal Research Center \u201cKrasnoyarsk Science Center of the Siberian Branch of the Russian Academy of Science\u201d, 660036 Krasnoyarsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1111\/j.1523-1755.2005.00365.x","article-title":"Definition and classification of chronic kidney disease: A position statement from Kidney Disease: Improving Global Outcomes (KDIGO)","volume":"67","author":"Levey","year":"2005","journal-title":"Kidney Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.cca.2019.08.030","article-title":"Chronic kidney disease: Biomarker diagnosis to therapeutic targets","volume":"499","author":"Wang","year":"2019","journal-title":"Clin. Chim. Acta"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1258\/acb.2009.009007","article-title":"How should proteinuria be detected and measured?","volume":"46","author":"Lamb","year":"2009","journal-title":"Ann. Clin. Biochem."},{"key":"ref_4","first-page":"22","article-title":"Differential diagnosis of chronic kidney disease (CKD): By primary diseases","volume":"54","author":"Uchida","year":"2011","journal-title":"Jpn. Med. Assoc. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.amjmed.2015.08.025","article-title":"Practical Approach to Detection and Management of Chronic Kidney Disease for the Primary Care Clinician","volume":"129","author":"Vassalotti","year":"2016","journal-title":"Am. J. Med."},{"key":"ref_6","first-page":"418","article-title":"The global burden of chronic kidney disease and the way forward","volume":"15","author":"Alebiosu","year":"2005","journal-title":"Ethn. Dis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"205435811879969","DOI":"10.1177\/2054358118799690","article-title":"Trends in Biopsy-Based Diagnosis of Kidney Disease: A Population Study","volume":"5","author":"Cunningham","year":"2018","journal-title":"Can. J. Kidney Health Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1053\/j.ajkd.2012.12.022","article-title":"Biomarkers in Nephrology: Core Curriculum 2013","volume":"62","author":"McMahon","year":"2013","journal-title":"Am. J. Kidney Dis."},{"key":"ref_9","first-page":"1143","article-title":"Classification of Renal Proteinuria: A Simple Algorithm","volume":"40","author":"Granados","year":"2002","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nrneph.2014.247","article-title":"Proteomic biomarkers in kidney disease: Issues in development and implementation","volume":"11","author":"Mischak","year":"2015","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dakna, M., Harris, K., Kalousis, A., Carpentier, S., Kolch, W., Schanstra, J.P., Haubitz, M., Vlahou, A., Mischak, H., and Girolami, M. (2010). Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers. BMC Bioinform., 11.","DOI":"10.1186\/1471-2105-11-594"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1074\/mcp.M110.001917","article-title":"Naturally Occurring Human Urinary Peptides for Use in Diagnosis of Chronic Kidney Disease","volume":"9","author":"Good","year":"2010","journal-title":"Mol. Cell. Proteom."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1681\/ASN.2014050423","article-title":"Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides","volume":"26","author":"Schanstra","year":"2015","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_14","first-page":"1510","article-title":"A urinary proteome-based classifier for the early detection of decline in glomerular filtration","volume":"32","author":"Pontillo","year":"2017","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"73","DOI":"10.3389\/fmed.2017.00073","article-title":"Rediscovering Beta-2 Microglobulin As a Biomarker across the Spectrum of Kidney Diseases","volume":"4","author":"Argyropoulos","year":"2017","journal-title":"Front. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1631\/jzus.B0900327","article-title":"Urinary proteomics as a novel tool for biomarker discovery in kidney diseases","volume":"11","author":"Wu","year":"2010","journal-title":"J. Zhejiang Univ. Sci. B"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1111\/j.1523-1755.2005.00335.x","article-title":"Urine protein patterns can serve as diagnostic tools in patients with IgA nephropathy","volume":"67","author":"Haubitz","year":"2005","journal-title":"Kidney Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1681\/ASN.2006070767","article-title":"Urine Biomarkers Predict the Cause of Glomerular Disease","volume":"18","author":"Varghese","year":"2007","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1146\/annurev.bioeng.8.061505.095802","article-title":"Machine Learning for Detection and Diagnosis of Disease","volume":"8","author":"Sajda","year":"2006","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/978-981-10-5562-1_10","article-title":"Development of New Diagnostic Techniques\u2014Machine Learning","volume":"Volume 1010","author":"Zhang","year":"2017","journal-title":"Substance and Non-substance Addiction"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1038\/s41591-018-0178-4","article-title":"New machine-learning technologies for computer-aided diagnosis","volume":"24","author":"Lynch","year":"2018","journal-title":"Nat. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1038\/s41591-018-0147-y","article-title":"Automated deep-neural-network surveillance of cranial images for acute neurologic events","volume":"24","author":"Titano","year":"2018","journal-title":"Nat. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"Ledsam","year":"2018","journal-title":"Nat. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"57","DOI":"10.5527\/wjn.v4.i1.57","article-title":"Biomarkers in chronic kidney disease, from kidney function to kidney damage","volume":"4","year":"2015","journal-title":"World J. Nephrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1111\/j.1464-5491.2007.02112.x","article-title":"The accuracy of cystatin C and commonly used creatinine-based methods for detecting moderate and mild chronic kidney disease in diabetes","volume":"24","author":"MacIsaac","year":"2007","journal-title":"Diabet. Med."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rysz, J., Gluba-Brz\u00f3zka, A., Franczyk, B., Jab\u0142onowski, Z., and Cia\u0142kowska-Rysz, A. (2017). Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome. Int. J. Mol. Sci., 18.","DOI":"10.3390\/ijms18081702"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1089\/omi.2013.0017","article-title":"Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology","volume":"17","author":"Swan","year":"2013","journal-title":"OMICS J. Integr. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.diabres.2013.07.006","article-title":"Clinical value of inflammatory urinary biomarkers in overt diabetic nephropathy: A prospective study","volume":"101","author":"Verhave","year":"2013","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pena, M.J., Heinzel, A., Heinze, G., Alkhalaf, A., Bakker, S.J.L., Nguyen, T.Q., Goldschmeding, R., Bilo, H.J.G., Perco, P., and Mayer, B. (2015). A Panel of Novel Biomarkers Representing Different Disease Pathways Improves Prediction of Renal Function Decline in Type 2 Diabetes. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0120995"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1136\/jcp.2005.035592","article-title":"Distinguishing diabetic nephropathy from other causes of glomerulosclerosis: An update","volume":"60","author":"Alsaad","year":"2007","journal-title":"J. Clin. Pathol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1038\/nbt.1511","article-title":"MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification","volume":"26","author":"Cox","year":"2008","journal-title":"Nat. Biotechnol."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/13\/4802\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:48:30Z","timestamp":1760176110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/13\/4802"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":31,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["ijms21134802"],"URL":"https:\/\/doi.org\/10.3390\/ijms21134802","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,7]]}}}