Text mining and computational chemistry reveal trends in applications and applicability of capillary electrophoresis

被引:3
|
作者
Palmblad, Magnus [1 ]
Vleugels, Reinier [2 ]
Bergquist, Jonas [3 ]
机构
[1] Leiden Univ, Ctr Prote & Metabol, Med Ctr, POB 9600, NL-2300 RC Leiden, Netherlands
[2] Vrije Univ, IBIVU Ctr Integrat Bioinformat, Dept Comp Sci, NL-1081 HV Amsterdam, Netherlands
[3] Uppsala Univ, Dept Chem, Analyt Chem & Neurochem, BMC, Box 599, SE-75124 Uppsala, Sweden
关键词
Capillary electrophoresis; Bibliometrics; Text mining; Machine learning; QSPR; Visualization;
D O I
10.1016/j.trac.2023.116946
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Capillary electrophoresis has matured into a highly sensitive and widely applied analytical method over the last forty years. Here we combine text mining and computational chemistry to paint, with very broad strokes, the applicability and trends in the scientific literature on capillary electrophoresis, simulta-neously demonstrating that this is not only possible, but reveal both expected and unexpected details of this history. All software and data are freely available on GitHub (https://github.com/ReinV/SCOPE) and OSF (https://osf.io/e56zt/). (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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页数:4
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