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Machine learning in oncology: a review
Machine learning is a set of techniques that promise to greatly enhance our data-processing capability. In the field of oncology, ML presents itself with a wealth of possible applications to the research and the clinical context, such as automated diagnosis and precise treatment modulation. In this...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cancer Intelligence
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373638/ https://www.ncbi.nlm.nih.gov/pubmed/32728381 http://dx.doi.org/10.3332/ecancer.2020.1065 |
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author | Nardini, Cecilia |
author_facet | Nardini, Cecilia |
author_sort | Nardini, Cecilia |
collection | PubMed |
description | Machine learning is a set of techniques that promise to greatly enhance our data-processing capability. In the field of oncology, ML presents itself with a wealth of possible applications to the research and the clinical context, such as automated diagnosis and precise treatment modulation. In this paper, we will review the principal applications of ML techniques in oncology and explore in detail how they work. This will allow us to discuss the issues and challenges that ML faces in this field, and ultimately gain a greater understanding of ML techniques and how they can improve oncological research and practice. |
format | Online Article Text |
id | pubmed-7373638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cancer Intelligence |
record_format | MEDLINE/PubMed |
spelling | pubmed-73736382020-07-28 Machine learning in oncology: a review Nardini, Cecilia Ecancermedicalscience Review Machine learning is a set of techniques that promise to greatly enhance our data-processing capability. In the field of oncology, ML presents itself with a wealth of possible applications to the research and the clinical context, such as automated diagnosis and precise treatment modulation. In this paper, we will review the principal applications of ML techniques in oncology and explore in detail how they work. This will allow us to discuss the issues and challenges that ML faces in this field, and ultimately gain a greater understanding of ML techniques and how they can improve oncological research and practice. Cancer Intelligence 2020-06-30 /pmc/articles/PMC7373638/ /pubmed/32728381 http://dx.doi.org/10.3332/ecancer.2020.1065 Text en © the authors; licensee ecancermedicalscience. http://creativecommons.org/licenses/by/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Nardini, Cecilia Machine learning in oncology: a review |
title | Machine learning in oncology: a review |
title_full | Machine learning in oncology: a review |
title_fullStr | Machine learning in oncology: a review |
title_full_unstemmed | Machine learning in oncology: a review |
title_short | Machine learning in oncology: a review |
title_sort | machine learning in oncology: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373638/ https://www.ncbi.nlm.nih.gov/pubmed/32728381 http://dx.doi.org/10.3332/ecancer.2020.1065 |
work_keys_str_mv | AT nardinicecilia machinelearninginoncologyareview |