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A decade in review: use of data analytics within the biopharmaceutical sector

There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the...

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Detalles Bibliográficos
Autores principales: Banner, Matthew, Alosert, Haneen, Spencer, Christopher, Cheeks, Matthew, Farid, Suzanne S, Thomas, Michael, Goldrick, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665905/
https://www.ncbi.nlm.nih.gov/pubmed/34926134
http://dx.doi.org/10.1016/j.coche.2021.100758
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author Banner, Matthew
Alosert, Haneen
Spencer, Christopher
Cheeks, Matthew
Farid, Suzanne S
Thomas, Michael
Goldrick, Stephen
author_facet Banner, Matthew
Alosert, Haneen
Spencer, Christopher
Cheeks, Matthew
Farid, Suzanne S
Thomas, Michael
Goldrick, Stephen
author_sort Banner, Matthew
collection PubMed
description There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.
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spelling pubmed-86659052021-12-15 A decade in review: use of data analytics within the biopharmaceutical sector Banner, Matthew Alosert, Haneen Spencer, Christopher Cheeks, Matthew Farid, Suzanne S Thomas, Michael Goldrick, Stephen Curr Opin Chem Eng Article There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted. Elsevier 2021-12 /pmc/articles/PMC8665905/ /pubmed/34926134 http://dx.doi.org/10.1016/j.coche.2021.100758 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Banner, Matthew
Alosert, Haneen
Spencer, Christopher
Cheeks, Matthew
Farid, Suzanne S
Thomas, Michael
Goldrick, Stephen
A decade in review: use of data analytics within the biopharmaceutical sector
title A decade in review: use of data analytics within the biopharmaceutical sector
title_full A decade in review: use of data analytics within the biopharmaceutical sector
title_fullStr A decade in review: use of data analytics within the biopharmaceutical sector
title_full_unstemmed A decade in review: use of data analytics within the biopharmaceutical sector
title_short A decade in review: use of data analytics within the biopharmaceutical sector
title_sort decade in review: use of data analytics within the biopharmaceutical sector
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665905/
https://www.ncbi.nlm.nih.gov/pubmed/34926134
http://dx.doi.org/10.1016/j.coche.2021.100758
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