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

被引：24

作者：

Banner, Matthew ^{[1
]}

Alosert, Haneen ^{[1
]}

Spencer, Christopher ^{[2
]}

Cheeks, Matthew ^{[2
]}

Farid, Suzanne S. ^{[1
]}

Thomas, Michael ^{[1
,3
]}

Goldrick, Stephen ^{[1
]}

机构：

[1] UCL, Dept Biochem Engn, Gower St, London WC1E 6BT, England

[2] AstraZeneca, Cell Culture Fermentat Sci, BioPharmaceut R&D, Biopharmaceut Dev, Cambridge, England

[3] UCL, London Ctr Nanotechnol, Gordon St, London WC1H 0AH, England

来源：

CURRENT OPINION IN CHEMICAL ENGINEERING | 2021年 / 34卷

基金：

英国生物技术与生命科学研究理事会;

关键词：

PARTIAL LEAST-SQUARES; PREDICTION; REGRESSION;

D O I：

10.1016/j.coche.2021.100758

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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.

引用

页数：10

共 46 条

[21] Systematic identification of metabolites controlling gene expression in E. coli
Lempp, Martin
Farke, Niklas
Kuntz, Michelle
Freibert, Sven Andreas
Lill, Roland
Link, Hannes
[J]. NATURE COMMUNICATIONS, 2019, 10 (1)
[22] Prediction of key state variables using support vector machines in bioprocesses
Li, YF
Yuan, JQ
[J]. CHEMICAL ENGINEERING & TECHNOLOGY, 2006, 29 (03) : 313 - 319
[23] Mahesh B., 2020, International Journal of Science and Research IJSR), V9, P381, DOI DOI 10.21275/ART20203995
[24] Scale-down model qualification of ambr® 250 high-throughput mini-bioreactor system for two commercial-scale mAb processes
Manahan, Matthew
Nelson, Michael
Cacciatore, Jonathan J.
Weng, Jessica
Xu, Sen
Pollard, Jennifer
[J]. BIOTECHNOLOGY PROGRESS, 2019, 35 (06)
[25] It's a noisy business! Genetic regulation at the nanomolar scale
McAdams, HH
Arkin, A
[J]. TRENDS IN GENETICS, 1999, 15 (02) : 65 - 69
[26] A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification
Mendez, Kevin M.
Reinke, Stacey N.
Broadhurst, David, I
[J]. METABOLOMICS, 2019, 15 (12)
[27] Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations
Mercier, Sarah M.
Diepenbroek, Bas
Wijffels, Rene H.
Streefland, Mathieu
[J]. TRENDS IN BIOTECHNOLOGY, 2014, 32 (06) : 329 - 336
[28] Machine learning for biochemical engineering: A review
Mowbray, Max
Savage, Thomas
Wu, Chufan
Song, Ziqi
Cho, Bovinille Anye
Del Rio-Chanona, Ehecatl A.
Zhang, Dongda
[J]. BIOCHEMICAL ENGINEERING JOURNAL, 2021, 172
[29] Multi-way partial least squares in monitoring batch processes
Nomikos, P
MacGregor, JF
[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1995, 30 (01) : 97 - 108
[30] Machine learning for targeted display advertising: transfer learning in action
Perlich, C.
Dalessandro, B.
Raeder, T.
Stitelman, O.
Provost, F.
[J]. MACHINE LEARNING, 2014, 95 (01) : 103 - 127

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