{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:55:45Z","timestamp":1790315745226,"version":"4.1.0"},"reference-count":55,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"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>In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result, we obtain a small set of seven of the most-useful biomarkers for diagnostics. They can also be used to build tests that avoid the costly and time-consuming existing methods. We summarize the current biological knowledge of the chosen biomarkers and contrast it with our findings.<\/jats:p>","DOI":"10.3390\/ijms23137057","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T09:00:13Z","timestamp":1656234013000},"page":"7057","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0729-9167","authenticated-orcid":false,"given":"Rados\u0142aw","family":"Piliszek","sequence":"first","affiliation":[{"name":"Computational Centre, University of Bia\u0142ystok, ul. Konstantego Cio\u0142kowskiego 1M, 15-245 Bia\u0142ystok, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3195-9965","authenticated-orcid":false,"given":"Anna A.","family":"Bro\u017cyna","sequence":"additional","affiliation":[{"name":"Department of Human Biology, Institute of Biology, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, ul. Lwowska 1, 87-100 Toru\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7928-4944","authenticated-orcid":false,"given":"Witold R.","family":"Rudnicki","sequence":"additional","affiliation":[{"name":"Computational Centre, University of Bia\u0142ystok, ul. Konstantego Cio\u0142kowskiego 1M, 15-245 Bia\u0142ystok, Poland"},{"name":"Institute of Computer Science, University of Bia\u0142ystok, ul. Konstantego Cio\u0142kowskiego 1M, 15-245 Bia\u0142ystok, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Saginala, K., Barsouk, A., Aluru, J.S., Rawla, P., Padala, S.A., and Barsouk, A. (2020). Epidemiology of bladder cancer. Med. Sci., 8.","DOI":"10.3390\/medsci8010015"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/nrc3817","article-title":"Molecular biology of bladder cancer: New insights into pathogenesis and clinical diversity","volume":"15","author":"Knowles","year":"2015","journal-title":"Nat. Rev. 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