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Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction
Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. Howe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295302/ https://www.ncbi.nlm.nih.gov/pubmed/34290286 http://dx.doi.org/10.1038/s41598-021-92864-y |
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author | Cheng, Li-Hsin Hsu, Te-Cheng Lin, Che |
author_facet | Cheng, Li-Hsin Hsu, Te-Cheng Lin, Che |
author_sort | Cheng, Li-Hsin |
collection | PubMed |
description | Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine. |
format | Online Article Text |
id | pubmed-8295302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82953022021-07-22 Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction Cheng, Li-Hsin Hsu, Te-Cheng Lin, Che Sci Rep Article Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine. Nature Publishing Group UK 2021-07-21 /pmc/articles/PMC8295302/ /pubmed/34290286 http://dx.doi.org/10.1038/s41598-021-92864-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cheng, Li-Hsin Hsu, Te-Cheng Lin, Che Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction |
title | Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction |
title_full | Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction |
title_fullStr | Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction |
title_full_unstemmed | Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction |
title_short | Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction |
title_sort | integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295302/ https://www.ncbi.nlm.nih.gov/pubmed/34290286 http://dx.doi.org/10.1038/s41598-021-92864-y |
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