Cargando…
Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria
Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing...
Autores principales: | Ford, Colby T., Janies, Daniel |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9274019/ https://www.ncbi.nlm.nih.gov/pubmed/35903243 http://dx.doi.org/10.12688/f1000research.21539.5 |
Ejemplares similares
-
Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model
por: Tang, Li, et al.
Publicado: (2020) -
DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
por: Poirion, Olivier B., et al.
Publicado: (2021) -
Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms
por: Zhang, Hanrui, et al.
Publicado: (2022) -
EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data
por: Zhang, Zhongyang, et al.
Publicado: (2019) -
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning
por: Hanson, Jack, et al.
Publicado: (2019)