NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.
about
Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.Improved methods for predicting peptide binding affinity to MHC class II molecules.NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.Insights into catalysis and function of phosphoribosyl-linked serine ubiquitination
P2860
NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.
description
2017 nî lūn-bûn
@nan
2017 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2017 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2017年の論文
@ja
2017年論文
@yue
2017年論文
@zh-hant
2017年論文
@zh-hk
2017年論文
@zh-mo
2017年論文
@zh-tw
2017年论文
@wuu
name
NNAlign: a platform to constru ...... receptor-ligand interactions.
@ast
NNAlign: a platform to constru ...... receptor-ligand interactions.
@en
type
label
NNAlign: a platform to constru ...... receptor-ligand interactions.
@ast
NNAlign: a platform to constru ...... receptor-ligand interactions.
@en
prefLabel
NNAlign: a platform to constru ...... receptor-ligand interactions.
@ast
NNAlign: a platform to constru ...... receptor-ligand interactions.
@en
P2860
P356
P1476
NNAlign: a platform to constru ...... f receptor-ligand interactions
@en
P2093
Massimo Andreatta
Morten Nielsen
P2860
P304
P356
10.1093/NAR/GKX276
P407
P577
2017-07-01T00:00:00Z