Cargando…
Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action
The rapid development of antimalarial resistance motivates the continued search for novel compounds with a mode of action (MoA) different to current antimalarials. Phenotypic screening has delivered thousands of promising hit compounds without prior knowledge of the compounds’ exact target or MoA. W...
Autores principales: | van Heerden, Ashleigh, van Wyk, Roelof, Birkholtz, Lyn-Marie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277430/ https://www.ncbi.nlm.nih.gov/pubmed/34268139 http://dx.doi.org/10.3389/fcimb.2021.688256 |
Ejemplares similares
-
Machine Learning Approaches Identify Chemical Features
for Stage-Specific Antimalarial Compounds
por: van Heerden, Ashleigh, et al.
Publicado: (2023) -
Streamlined and Robust Stage-Specific Profiling of Gametocytocidal Compounds Against Plasmodium falciparum
por: Reader, Janette, et al.
Publicado: (2022) -
Adapt or Die: Targeting Unique Transmission-Stage Biology for Malaria Elimination
por: van der Watt, Mariëtte E., et al.
Publicado: (2022) -
Targeting the Plasmodium falciparum’s Thymidylate Monophosphate Kinase for the Identification of Novel Antimalarial Natural Compounds
por: Enninful, Kweku S., et al.
Publicado: (2022) -
MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum
por: van Wyk, Roelof, et al.
Publicado: (2021)