Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.

de Cripan, Sara M; Cereto-Massagué, Adrià; Herrero, Pol; Barcaru, Andrei; Canela, Núria; Domingo-Almenara, Xavier

de Cripan, Sara M; Cereto-Massagué, Adrià; Herrero, Pol; Barcaru, Andrei; Canela, Núria; Domingo-Almenara, Xavier.

Afiliação

de Cripan SM; Computational Metabolomics for Systems Biology Lab, Omics Sciences Unit, Eurecat-Technology Centre of Catalonia, 08005 Barcelona, Catalonia, Spain.
Cereto-Massagué A; Centre for Omics Sciences (COS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, Unique Scientific and Technical Infrastructures (ICTS), 43204 Reus, Catalonia, Spain.
Herrero P; Department of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain.
Barcaru A; Centre for Omics Sciences (COS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, Unique Scientific and Technical Infrastructures (ICTS), 43204 Reus, Catalonia, Spain.
Canela N; Centre for Omics Sciences (COS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, Unique Scientific and Technical Infrastructures (ICTS), 43204 Reus, Catalonia, Spain.
Domingo-Almenara X; Independent Researcher, 1012 WX Amsterdam, The Netherlands.

Biomedicines ; 10(4)2022 Apr 11.

Article em En | MEDLINE | ID: mdl-35453629

Palavras-chave

GC-MS; machine-learning; metabolomics; retention index; retention time

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Biomedicines Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Espanha

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