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Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318243/ https://www.ncbi.nlm.nih.gov/pubmed/34320000 http://dx.doi.org/10.1371/journal.pone.0254596 |
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author | Venezian Povoa, Lucas Ribeiro, Carlos Henrique Costa da Silva, Israel Tojal |
author_facet | Venezian Povoa, Lucas Ribeiro, Carlos Henrique Costa da Silva, Israel Tojal |
author_sort | Venezian Povoa, Lucas |
collection | PubMed |
description | Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers through Machine Learning (ML) algorithms still remains to be fully explored. Here, we present a Multi Learning Training approach (MuLT) combining supervised, unsupervised and self-supervised learning algorithms, to examine the predictive value of heterogeneous treatment outcomes for Multiple Myeloma (MM). We show that gene expression values improve the treatment sensitivity prediction and recapitulates genetic abnormalities detected by Fluorescence in situ hybridization (FISH) testing. MuLT performance was assessed by cross-validation experiments, in which it predicted treatment sensitivity with 68.70% of AUC. Finally, simulations showed numerical evidences that in average 17.07% of patients could get better response to a different treatment at the first line. |
format | Online Article Text |
id | pubmed-8318243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83182432021-07-31 Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response Venezian Povoa, Lucas Ribeiro, Carlos Henrique Costa da Silva, Israel Tojal PLoS One Research Article Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers through Machine Learning (ML) algorithms still remains to be fully explored. Here, we present a Multi Learning Training approach (MuLT) combining supervised, unsupervised and self-supervised learning algorithms, to examine the predictive value of heterogeneous treatment outcomes for Multiple Myeloma (MM). We show that gene expression values improve the treatment sensitivity prediction and recapitulates genetic abnormalities detected by Fluorescence in situ hybridization (FISH) testing. MuLT performance was assessed by cross-validation experiments, in which it predicted treatment sensitivity with 68.70% of AUC. Finally, simulations showed numerical evidences that in average 17.07% of patients could get better response to a different treatment at the first line. Public Library of Science 2021-07-28 /pmc/articles/PMC8318243/ /pubmed/34320000 http://dx.doi.org/10.1371/journal.pone.0254596 Text en © 2021 Venezian Povoa et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Venezian Povoa, Lucas Ribeiro, Carlos Henrique Costa da Silva, Israel Tojal Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response |
title | Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response |
title_full | Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response |
title_fullStr | Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response |
title_full_unstemmed | Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response |
title_short | Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response |
title_sort | machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318243/ https://www.ncbi.nlm.nih.gov/pubmed/34320000 http://dx.doi.org/10.1371/journal.pone.0254596 |
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