A decade in review: use of data analytics within the biopharmaceutical sector.
A decade in review: use of data analytics within the biopharmaceutical sector.
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DOI：
10.1016/j.coche.2021.100758
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发表时间：
2021-12
影响因子：
6.6
通讯作者：
Goldrick S
中科院分区：
文献类型：
--
作者：
Banner M;Alosert H;Spencer C;Cheeks M;Farid SS;Thomas M;Goldrick S
近年来，数据分析在生物制药领域显着增长。算法利用率和数据大小之间没有观察到明显的趋势。 PLS 被认为是生物制药领域应用最广泛的算法。大多数数据分析应用程序都集中在 USP 运营上。随着该行业向工业 4.0 转型，数据分析将发挥关键作用。生物制药领域产生大量数据。传统上，被标记为多元数据分析的数据分析方法一直是用于询问这些复杂数据集的标准统计技术。然而，最近，越来越多地使用更广泛的机器学习算法来进一步利用这些数据。在本文中，通过回顾过去十年内发表的期刊文章和专利来评估生物制药领域数据分析技术的采用情况。论文的目标是确定生物制药领域不同应用领域中应用的最主要算法，并探讨数据集大小和采用的算法之间是否存在趋势。
Data analytics has increasing significantly in recent years in the biopharma sector. No clear trend observed between algorithm utilisation and data size. PLS was found to be most applied algorithm within the biopharmaceutical sector. Majority of the data analytics applications are focused on USP operations. Data analytics will play a key role as the sector transitions towards Industry 4.0. 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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