Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
about
SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence.Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes.Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.
P2860
Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
@ast
Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
@en
type
label
Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
@ast
Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
@en
prefLabel
Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
@ast
Positive-unlabeled learning for the prediction of conformational B-cell epitopes.
@en
P2860
P1433
P1476
Positive-unlabeled learning for the prediction of conformational B-cell epitopes
@en
P2093
P2860
P2888
P356
10.1186/1471-2105-16-S18-S12
P478
16 Suppl 18
P577
2015-12-09T00:00:00Z
P6179
1021765486