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A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning

  • 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.

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Metadaten
Author:Vincent SchillingORCiD, Peter Beyerlein, Jeremy ChienORCiD
URN:urn:nbn:de:kobv:526-opus4-17751
DOI (Version of Record):https://doi.org/10.3390/a16070330
Parent Title (English):Algorithms
Publisher:MDPI
Document Type:Article
Language:English
Year of Publication:2023
Publishing Institution:Technische Hochschule Wildau
Release Date:2023/07/25
Tag:SHAP; diagnostic biomarkers; machine learning; ovarian cancer; platinum resistance
Volume:16
Issue:7
Article Number:330
Source: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
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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