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Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells
Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various form...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527086/ https://www.ncbi.nlm.nih.gov/pubmed/34691155 http://dx.doi.org/10.3389/fgene.2021.742902 |
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author | Jung, Hae Deok Sung, Yoo Jin Kim, Hyun Uk |
author_facet | Jung, Hae Deok Sung, Yoo Jin Kim, Hyun Uk |
author_sort | Jung, Hae Deok |
collection | PubMed |
description | Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells. |
format | Online Article Text |
id | pubmed-8527086 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85270862021-10-21 Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells Jung, Hae Deok Sung, Yoo Jin Kim, Hyun Uk Front Genet Genetics Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells. Frontiers Media S.A. 2021-10-06 /pmc/articles/PMC8527086/ /pubmed/34691155 http://dx.doi.org/10.3389/fgene.2021.742902 Text en Copyright © 2021 Jung, Sung and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Jung, Hae Deok Sung, Yoo Jin Kim, Hyun Uk Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells |
title | Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells |
title_full | Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells |
title_fullStr | Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells |
title_full_unstemmed | Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells |
title_short | Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells |
title_sort | omics and computational modeling approaches for the effective treatment of drug-resistant cancer cells |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527086/ https://www.ncbi.nlm.nih.gov/pubmed/34691155 http://dx.doi.org/10.3389/fgene.2021.742902 |
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