Cargando…
Residue–Residue Interaction Prediction via Stacked Meta-Learning
Protein–protein interactions (PPIs) are the basis of most biological functions determined by residue–residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered ine...
Autores principales: | Chen, Kuan-Hsi, Hu, Yuh-Jyh |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232778/ https://www.ncbi.nlm.nih.gov/pubmed/34203772 http://dx.doi.org/10.3390/ijms22126393 |
Ejemplares similares
-
Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme
por: Chen, Kuan-Hsi, et al.
Publicado: (2019) -
Stacked survival models for residual lifetime data
por: McVittie, James H., et al.
Publicado: (2022) -
Pattern to Knowledge: Deep Knowledge-Directed Machine Learning for Residue-Residue Interaction Prediction
por: Wong, Andrew K. C., et al.
Publicado: (2018) -
Computational Prediction of Heme-Binding Residues by Exploiting Residue Interaction Network
por: Liu, Rong, et al.
Publicado: (2011) -
Evolutionary conservation of DNA-contact residues in DNA-binding domains
por: Chang, Yao-Lin, et al.
Publicado: (2008)