[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119127-en":3,"doc-seo-119127-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119127,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning","Biomarker identification is essential for improving cancer diagnosis, clarifying underlying biological mechanisms, and enabling targeted therapies. This study presents a machine learning framework that predicts ovarian cancer patient outcomes and platinum resistance status from publicly available gene expression datasets. Six classical machine-learning algorithms are evaluated and the best-performing models are interpreted using SHAP feature importance. Selected genes linked to outcomes and platinum resistance are further validated with Kaplan–Meier analyses. Model performance is reported as higher than comparable approaches, yielding promising biomarker candidates including TMEFF2, ACSM3, SLC4A1, and ALDH4A1.","algorithms  \nArticle  \nA Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning  \nVincent Schilling 1,2, *, Peter Beyerlein 3 and Jeremy Chien 2, *  \nCitation: Schilling, V.; Beyerlein, P.; Chien, J. A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning. Algorithms 2023, 16, 330 . [https://doi.org/10.3390/a16070330](https://doi.org/10.3390/a16070330)  \nAcademic Editor: Frank Werner  \nReceived: 30 April 2023  \nRevised: 6 July 2023  \nAccepted: 7 July 2023  \nPublished: 11 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Engineering and Natural Sciences, Technical University of Applied Sciences Wildau,  \n15745 Wildau, Germany  \n2 Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95817, USA  \n3 Ibiomics UG, 14193 Berlin, Germany; [peter.beyerlein@googlemail.com](peter.beyerlein@googlemail.com)  \n* [Correspondence: vschilling@ucdavis.edu](Correspondence: vschilling@ucdavis.edu) (V.S.); [jrchien@ucdavis.edu](jrchien@ucdavis.edu) (J.C.)  \nAbstract: 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' 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–Meier 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 .  \nKeywords: ovarian cancer; machine learning; SHAP; diagnostic biomarkers; platinum resistance  \n1. Introduction  \nOvarian cancer is the most lethal gynecologic malignancy, with a five-year survival rate of 49% for all stages of the cancer [1] . Ovarian cancer is very aggressive and often recurs after subsequent treatments. Most patients will acquire resistance through treatment consisting of carboplatin-based chemotherapy as well as PARP inhibitors [2,3] .  \nOvarian cancer is frequently diagnosed at advanced FIGO stages, which leads to overall poor survival rates. The symptoms are generally non-specific, and therefore early detection methods, genetic screening, and multiple treatment options are needed to improve the outcomes of patients with ovarian cancer [4,5] .  \nRecent studies have demonstrated that biological parameters like mRNA gene expression can be linked to and predict the outcome of cancer patients [6–8] . For that matter, statistical methods have been used, as well as machine learning methods [9] . Studies have shown that machine learning models achieve high performance in predicting potential biomarkers, the stage of cancer, platinum sensitivity, relapse time, as well as overall survival (OS) in ovarian cancer using gene expression profiles, image data, copy number variations, and more [10–14] .  \nIn a previous study by Spentzos et al. [15], the SPLASH algorithm was used for the initial discovery of candidate biomarkers from mRNA gene expression analysis and weighted voting and k nearest neighbor for training and leave-one-out cross-validation for predictive accuracy. In another study by Hartmann et al. [11], a supervised machinelearning approach using the supp","cbCaiu176sny3fvz","https://ap.wps.com/l/cbCaiu176sny3fvz","pdf",6054464,1,20,"English","en",105,"# Introduction\n## Biomarker identification and clinical need\n## Machine learning and gene-expression based predictors\n## Prior approaches in ovarian cancer data","[{\"question\":\"What problem does the study address in ovarian cancer research?\",\"answer\":\"The study focuses on identifying biomarkers and predicting ovarian cancer outcomes and platinum resistance, which are critical for diagnosis and therapy selection.\"},{\"question\":\"How are machine-learning models evaluated and interpreted?\",\"answer\":\"Six classical machine-learning algorithms are compared for predictive performance, and the best model(s) are analyzed using SHAP feature importance to identify influential genes.\"},{\"question\":\"How are the selected genes validated?\",\"answer\":\"The candidate genes correlated with outcomes and platinum resistance are validated using Kaplan–Meier plots.\"}]","A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning | 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problem does the study address in ovarian cancer research?","Question",{"text":76,"@type":77},"The study focuses on identifying biomarkers and predicting ovarian cancer outcomes and platinum resistance, which are critical for diagnosis and therapy selection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are machine-learning models evaluated and interpreted?",{"text":81,"@type":77},"Six classical machine-learning algorithms are compared for predictive performance, and the best model(s) are analyzed using SHAP feature importance to identify influential genes.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the selected genes validated?",{"text":85,"@type":77},"The candidate genes correlated with outcomes and platinum resistance are validated using Kaplan–Meier 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