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Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer

Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment. However, there is still room for improvement in the performance of the existing models based on the aforementioned comb...

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Detalles Bibliográficos
Autores principales: Khan, Deeba, Shedole, Seema
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147748/
https://www.ncbi.nlm.nih.gov/pubmed/35629097
http://dx.doi.org/10.3390/jpm12050674
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author Khan, Deeba
Shedole, Seema
author_facet Khan, Deeba
Shedole, Seema
author_sort Khan, Deeba
collection PubMed
description Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment. However, there is still room for improvement in the performance of the existing models based on the aforementioned combination. In this work, we propose two models that complement the treatment of breast cancer patients. First, we discuss our deep learning-based model for breast cancer subtype classification. Second, we propose DCNN-DR, a deep convolute.ion neural network-drug response method for predicting the effectiveness of drugs on in vitro and in vivo breast cancer datasets. Finally, we applied DCNN-DR for predicting effective drugs for the basal-like breast cancer subtype and validated the results with the information available in the literature. The models proposed use late integration methods and have fairly better predictive performance compared to the existing methods. We use the Pearson correlation coefficient and accuracy as the performance measures for the regression and classification models, respectively.
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spelling pubmed-91477482022-05-29 Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer Khan, Deeba Shedole, Seema J Pers Med Article Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment. However, there is still room for improvement in the performance of the existing models based on the aforementioned combination. In this work, we propose two models that complement the treatment of breast cancer patients. First, we discuss our deep learning-based model for breast cancer subtype classification. Second, we propose DCNN-DR, a deep convolute.ion neural network-drug response method for predicting the effectiveness of drugs on in vitro and in vivo breast cancer datasets. Finally, we applied DCNN-DR for predicting effective drugs for the basal-like breast cancer subtype and validated the results with the information available in the literature. The models proposed use late integration methods and have fairly better predictive performance compared to the existing methods. We use the Pearson correlation coefficient and accuracy as the performance measures for the regression and classification models, respectively. MDPI 2022-04-22 /pmc/articles/PMC9147748/ /pubmed/35629097 http://dx.doi.org/10.3390/jpm12050674 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Khan, Deeba
Shedole, Seema
Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer
title Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer
title_full Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer
title_fullStr Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer
title_full_unstemmed Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer
title_short Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer
title_sort leveraging deep learning techniques and integrated omics data for tailored treatment of breast cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147748/
https://www.ncbi.nlm.nih.gov/pubmed/35629097
http://dx.doi.org/10.3390/jpm12050674
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