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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...

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Detalles Bibliográficos
Autores principales: Yao, Sijie, Cao, Biwei, Li, Tingyi, Kalos, Denise, Yuan, Yading, Wang, Xuefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
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.
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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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