Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.

van Heerden, Ashleigh; van Wyk, Roelof; Birkholtz, Lyn-Marie

van Heerden, Ashleigh; van Wyk, Roelof; Birkholtz, Lyn-Marie.

Affiliation

van Heerden A; Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South Africa.
van Wyk R; University of Pretoria Institute for Sustainable Malaria Control, University of Pretoria, Hatfield, South Africa.
Birkholtz LM; Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South Africa.

Front Cell Infect Microbiol ; 11: 688256, 2021.

Article in En | MEDLINE | ID: mdl-34268139

Subject(s)

Antimalarials; Malaria; Antimalarials/pharmacology; Drug Discovery; Humans; Machine Learning; Transcriptome

Key words

biomarker; chemo-transcriptomic fingerprint; gene expression profile; machine learning; mode of action; multinominal logistic regression

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Malaria / Antimalarials Type of study: Prognostic_studies Limits: Humans Language: En Journal: Front Cell Infect Microbiol Year: 2021 Type: Article Affiliation country: South Africa

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