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Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of trea...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975164/ https://www.ncbi.nlm.nih.gov/pubmed/36873878 http://dx.doi.org/10.3389/fmed.2023.1086097 |
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author | Partin, Alexander Brettin, Thomas S. Zhu, Yitan Narykov, Oleksandr Clyde, Austin Overbeek, Jamie Stevens, Rick L. |
author_facet | Partin, Alexander Brettin, Thomas S. Zhu, Yitan Narykov, Oleksandr Clyde, Austin Overbeek, Jamie Stevens, Rick L. |
author_sort | Partin, Alexander |
collection | PubMed |
description | Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths. |
format | Online Article Text |
id | pubmed-9975164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99751642023-03-02 Deep learning methods for drug response prediction in cancer: Predominant and emerging trends Partin, Alexander Brettin, Thomas S. Zhu, Yitan Narykov, Oleksandr Clyde, Austin Overbeek, Jamie Stevens, Rick L. Front Med (Lausanne) Medicine Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths. Frontiers Media S.A. 2023-02-15 /pmc/articles/PMC9975164/ /pubmed/36873878 http://dx.doi.org/10.3389/fmed.2023.1086097 Text en Copyright © 2023 Partin, Brettin, Zhu, Narykov, Clyde, Overbeek and Stevens. 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 | Medicine Partin, Alexander Brettin, Thomas S. Zhu, Yitan Narykov, Oleksandr Clyde, Austin Overbeek, Jamie Stevens, Rick L. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends |
title | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends |
title_full | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends |
title_fullStr | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends |
title_full_unstemmed | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends |
title_short | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends |
title_sort | deep learning methods for drug response prediction in cancer: predominant and emerging trends |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975164/ https://www.ncbi.nlm.nih.gov/pubmed/36873878 http://dx.doi.org/10.3389/fmed.2023.1086097 |
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