Schilling, V.; Beyerlein, P.; Chien, J. A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning. Algorithms2023, 16, 330.
Schilling, V.; Beyerlein, P.; Chien, J. A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning. Algorithms 2023, 16, 330.
Schilling, V.; Beyerlein, P.; Chien, J. A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning. Algorithms2023, 16, 330.
Schilling, V.; Beyerlein, P.; Chien, J. A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning. Algorithms 2023, 16, 330.
Abstract
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 the outcome and platinum resistance status of ovarian cancer patients using public 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 were correlating 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 were 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.
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
