Cargando…
Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models
Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial acti...
Autores principales: | Urista, Diana V., Carrué, Diego B., Otero, Iago, Arrasate, Sonia, Quevedo-Tumailli, Viviana F., Gestal, Marcos, González-Díaz, Humbert, Munteanu, Cristian R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465777/ https://www.ncbi.nlm.nih.gov/pubmed/32751710 http://dx.doi.org/10.3390/biology9080198 |
Ejemplares similares
-
Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
por: Munteanu, Cristian R., et al.
Publicado: (2021) -
IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds
por: Quevedo-Tumailli, Viviana, et al.
Publicado: (2021) -
Perturbation-Theory Machine Learning (PTML) Multilabel
Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma
Compounds
por: Cabrera-Andrade, Alejandro, et al.
Publicado: (2020) -
A methodology for the design of experiments in computational intelligence with multiple regression models
por: Fernandez-Lozano, Carlos, et al.
Publicado: (2016) -
Polymeric Nanocarriers for the Delivery of Antimalarials
por: Mhlwatika, Zandile, et al.
Publicado: (2018)