Cargando…
Predicting anticancer hyperfoods with graph convolutional networks
BACKGROUND: Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to lear...
Autores principales: | Gonzalez, Guadalupe, Gong, Shunwang, Laponogov, Ivan, Bronstein, Michael, Veselkov, Kirill |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182908/ https://www.ncbi.nlm.nih.gov/pubmed/34099048 http://dx.doi.org/10.1186/s40246-021-00333-4 |
Ejemplares similares
-
Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19
por: Laponogov, Ivan, et al.
Publicado: (2021) -
HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods
por: Veselkov, Kirill, et al.
Publicado: (2019) -
Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient
por: Southern, Joshua, et al.
Publicado: (2023) -
Exploiting and assessing multi-source data for supervised biomedical named entity recognition
por: Galea, Dieter, et al.
Publicado: (2018) -
Alzheimer’s disease: using gene/protein network machine learning for molecule discovery in olive oil
por: Rita, Luís, et al.
Publicado: (2023)