Precision oncology: a review to assess interpretability in several explainable methods

Great efforts have been made to develop precision medicine-based treatments using machine learning. In this field, where the goal is to provide the optimal treatment for each patient based on his/her medical history and genomic characteristics, it is not sufficient to make excellent predictions. The...

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Detalles Bibliográficos
Autores: Gimeno-Combarro, M. (Marian)|||/items/aee49dbe-e8c2-4ab3-bf3f-3129a14e3309, Sada-del-Real, K. (Katyna)|||/items/600b4a96-0d20-4e9a-86d8-20c609d613e5, Rubio-Díaz-Cordovés, A. (Ángel)|||/items/7d740e1e-38db-46ea-9834-8c61aa6eedee
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/69414
Acceso en línea:https://hdl.handle.net/10171/69414
Access Level:acceso abierto
Palabra clave:Assignment problem
Drug recommendation
Explainable artificial intelligence
Interpretability
Machine learning
Method comparison
Precision medicine
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spelling Precision oncology: a review to assess interpretability in several explainable methodsGimeno-Combarro, M. (Marian)|||/items/aee49dbe-e8c2-4ab3-bf3f-3129a14e3309Sada-del-Real, K. (Katyna)|||/items/600b4a96-0d20-4e9a-86d8-20c609d613e5Rubio-Díaz-Cordovés, A. (Ángel)|||/items/7d740e1e-38db-46ea-9834-8c61aa6eedeeAssignment problemDrug recommendationExplainable artificial intelligenceInterpretabilityMachine learningMethod comparisonPrecision medicineGreat efforts have been made to develop precision medicine-based treatments using machine learning. In this field, where the goal is to provide the optimal treatment for each patient based on his/her medical history and genomic characteristics, it is not sufficient to make excellent predictions. The challenge is to understand and trust the model's decisions while also being able to easily implement it. However, one of the issues with machine learning algorithms-particularly deep learning-is their lack of interpretability. This review compares six different machine learning methods to provide guidance for defining interpretability by focusing on accuracy, multi-omics capability, explainability and implementability. Our selection of algorithms includes tree-, regression- and kernel-based methods, which we selected for their ease of interpretation for the clinician. We also included two novel explainable methods in the comparison. No significant differences in accuracy were observed when comparing the methods, but an improvement was observed when using gene expression instead of mutational status as input for these methods. We concentrated on the current intriguing challenge: model comprehension and ease of use. Our comparison suggests that the tree-based methods are the most interpretable of those tested.Oxford University PressDadun. Depósito Académico Digital Universidad de Navarra20242024-04-3020232023-01-0120232023-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/69414reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/694142026-06-21T12:47:57Z
dc.title.none.fl_str_mv Precision oncology: a review to assess interpretability in several explainable methods
title Precision oncology: a review to assess interpretability in several explainable methods
spellingShingle Precision oncology: a review to assess interpretability in several explainable methods
Gimeno-Combarro, M. (Marian)|||/items/aee49dbe-e8c2-4ab3-bf3f-3129a14e3309
Assignment problem
Drug recommendation
Explainable artificial intelligence
Interpretability
Machine learning
Method comparison
Precision medicine
title_short Precision oncology: a review to assess interpretability in several explainable methods
title_full Precision oncology: a review to assess interpretability in several explainable methods
title_fullStr Precision oncology: a review to assess interpretability in several explainable methods
title_full_unstemmed Precision oncology: a review to assess interpretability in several explainable methods
title_sort Precision oncology: a review to assess interpretability in several explainable methods
dc.creator.none.fl_str_mv Gimeno-Combarro, M. (Marian)|||/items/aee49dbe-e8c2-4ab3-bf3f-3129a14e3309
Sada-del-Real, K. (Katyna)|||/items/600b4a96-0d20-4e9a-86d8-20c609d613e5
Rubio-Díaz-Cordovés, A. (Ángel)|||/items/7d740e1e-38db-46ea-9834-8c61aa6eedee
author Gimeno-Combarro, M. (Marian)|||/items/aee49dbe-e8c2-4ab3-bf3f-3129a14e3309
author_facet Gimeno-Combarro, M. (Marian)|||/items/aee49dbe-e8c2-4ab3-bf3f-3129a14e3309
Sada-del-Real, K. (Katyna)|||/items/600b4a96-0d20-4e9a-86d8-20c609d613e5
Rubio-Díaz-Cordovés, A. (Ángel)|||/items/7d740e1e-38db-46ea-9834-8c61aa6eedee
author_role author
author2 Sada-del-Real, K. (Katyna)|||/items/600b4a96-0d20-4e9a-86d8-20c609d613e5
Rubio-Díaz-Cordovés, A. (Ángel)|||/items/7d740e1e-38db-46ea-9834-8c61aa6eedee
author2_role author
author
dc.contributor.none.fl_str_mv Dadun. Depósito Académico Digital Universidad de Navarra
dc.subject.none.fl_str_mv Assignment problem
Drug recommendation
Explainable artificial intelligence
Interpretability
Machine learning
Method comparison
Precision medicine
topic Assignment problem
Drug recommendation
Explainable artificial intelligence
Interpretability
Machine learning
Method comparison
Precision medicine
description Great efforts have been made to develop precision medicine-based treatments using machine learning. In this field, where the goal is to provide the optimal treatment for each patient based on his/her medical history and genomic characteristics, it is not sufficient to make excellent predictions. The challenge is to understand and trust the model's decisions while also being able to easily implement it. However, one of the issues with machine learning algorithms-particularly deep learning-is their lack of interpretability. This review compares six different machine learning methods to provide guidance for defining interpretability by focusing on accuracy, multi-omics capability, explainability and implementability. Our selection of algorithms includes tree-, regression- and kernel-based methods, which we selected for their ease of interpretation for the clinician. We also included two novel explainable methods in the comparison. No significant differences in accuracy were observed when comparing the methods, but an improvement was observed when using gene expression instead of mutational status as input for these methods. We concentrated on the current intriguing challenge: model comprehension and ease of use. Our comparison suggests that the tree-based methods are the most interpretable of those tested.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01
2023
2023-01-01
2024
2024-04-30
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10171/69414
url https://hdl.handle.net/10171/69414
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Dadun. Depósito Académico Digital de la Universidad de Navarra
instname:Universidad de Navarra
instname_str Universidad de Navarra
reponame_str Dadun. Depósito Académico Digital de la Universidad de Navarra
collection Dadun. Depósito Académico Digital de la Universidad de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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