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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9147748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT khandeeba leveragingdeeplearningtechniquesandintegratedomicsdatafortailoredtreatmentofbreastcancer AT shedoleseema leveragingdeeplearningtechniquesandintegratedomicsdatafortailoredtreatmentofbreastcancer |