{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T20:35:01Z","timestamp":1769286901663,"version":"3.49.0"},"reference-count":76,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients\u2019 outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan\u2013Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.<\/jats:p>","DOI":"10.3390\/a16070330","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T01:01:41Z","timestamp":1689123701000},"page":"330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6992-4805","authenticated-orcid":false,"given":"Vincent","family":"Schilling","sequence":"first","affiliation":[{"name":"Department of Engineering and Natural Sciences, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany"},{"name":"Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95817, USA"}]},{"given":"Peter","family":"Beyerlein","sequence":"additional","affiliation":[{"name":"Ibiomics UG, 14193 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4744-8374","authenticated-orcid":false,"given":"Jeremy","family":"Chien","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95817, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"ref_1","unstructured":"(2023, March 28). 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