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Prediction-oriented prognostic biomarker discovery with survival machine learning methods
Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing pred...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10273194/ https://www.ncbi.nlm.nih.gov/pubmed/37332657 http://dx.doi.org/10.1093/nargab/lqad055 |
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author | Yao, Sijie Cao, Biwei Li, Tingyi Kalos, Denise Yuan, Yading Wang, Xuefeng |
author_facet | Yao, Sijie Cao, Biwei Li, Tingyi Kalos, Denise Yuan, Yading Wang, Xuefeng |
author_sort | Yao, Sijie |
collection | PubMed |
description | Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme gradient boosting, light gradient boosting and deep learning-based survival models. Additionally, we adapt the recently proposed prediction-oriented marker selection (PROMISE) to a survival model (PROMISE-Cox) as a benchmark approach. Our simulation studies indicate that boosting-based approaches tend to provide superior accuracy with better true positive rate and false positive rate in more complicated scenarios. For demonstration purpose, we applied the proposed biomarker selection strategies to identify prognostic biomarkers in different modalities of head and neck cancer data. |
format | Online Article Text |
id | pubmed-10273194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102731942023-06-17 Prediction-oriented prognostic biomarker discovery with survival machine learning methods Yao, Sijie Cao, Biwei Li, Tingyi Kalos, Denise Yuan, Yading Wang, Xuefeng NAR Genom Bioinform Methods Article Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme gradient boosting, light gradient boosting and deep learning-based survival models. Additionally, we adapt the recently proposed prediction-oriented marker selection (PROMISE) to a survival model (PROMISE-Cox) as a benchmark approach. Our simulation studies indicate that boosting-based approaches tend to provide superior accuracy with better true positive rate and false positive rate in more complicated scenarios. For demonstration purpose, we applied the proposed biomarker selection strategies to identify prognostic biomarkers in different modalities of head and neck cancer data. Oxford University Press 2023-06-16 /pmc/articles/PMC10273194/ /pubmed/37332657 http://dx.doi.org/10.1093/nargab/lqad055 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Article Yao, Sijie Cao, Biwei Li, Tingyi Kalos, Denise Yuan, Yading Wang, Xuefeng Prediction-oriented prognostic biomarker discovery with survival machine learning methods |
title | Prediction-oriented prognostic biomarker discovery with survival machine learning methods |
title_full | Prediction-oriented prognostic biomarker discovery with survival machine learning methods |
title_fullStr | Prediction-oriented prognostic biomarker discovery with survival machine learning methods |
title_full_unstemmed | Prediction-oriented prognostic biomarker discovery with survival machine learning methods |
title_short | Prediction-oriented prognostic biomarker discovery with survival machine learning methods |
title_sort | prediction-oriented prognostic biomarker discovery with survival machine learning methods |
topic | Methods Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10273194/ https://www.ncbi.nlm.nih.gov/pubmed/37332657 http://dx.doi.org/10.1093/nargab/lqad055 |
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