Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility

Detalles Bibliográficos
Autores principales: Meid, Andreas D., Gerharz, Alexander, Groll, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Perspectives
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923723/
https://www.ncbi.nlm.nih.gov/pubmed/35104394
http://dx.doi.org/10.1002/psp4.12761
_version_ 1784669719285989376
author Meid, Andreas D.
Gerharz, Alexander
Groll, Andreas
author_facet Meid, Andreas D.
Gerharz, Alexander
Groll, Andreas
author_sort Meid, Andreas D.
collection PubMed
description
format Online
Article
Text
id pubmed-8923723
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-89237232022-03-21 Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility Meid, Andreas D. Gerharz, Alexander Groll, Andreas CPT Pharmacometrics Syst Pharmacol Perspectives John Wiley and Sons Inc. 2022-02-01 2022-03 /pmc/articles/PMC8923723/ /pubmed/35104394 http://dx.doi.org/10.1002/psp4.12761 Text en © 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Perspectives
Meid, Andreas D.
Gerharz, Alexander
Groll, Andreas
Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility
title Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility
title_full Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility
title_fullStr Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility
title_full_unstemmed Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility
title_short Machine learning for tumor growth inhibition: Interpretable predictive models for transparency and reproducibility
title_sort machine learning for tumor growth inhibition: interpretable predictive models for transparency and reproducibility
topic Perspectives
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923723/
https://www.ncbi.nlm.nih.gov/pubmed/35104394
http://dx.doi.org/10.1002/psp4.12761
work_keys_str_mv AT meidandreasd machinelearningfortumorgrowthinhibitioninterpretablepredictivemodelsfortransparencyandreproducibility
AT gerharzalexander machinelearningfortumorgrowthinhibitioninterpretablepredictivemodelsfortransparencyandreproducibility
AT grollandreas machinelearningfortumorgrowthinhibitioninterpretablepredictivemodelsfortransparencyandreproducibility

Ejemplares similares