Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
about
Improving landscape inference by integrating heterogeneous data in the inverse Ising problem.Entropy is a Simple Measure of the Antibody Profile and is an Indicator of Health Status: A Proof of Concept.Polymorphic sites preferentially avoid co-evolving residues in MHC class I proteins.Humanization of Antibodies using a Statistical Inference Approach
P2860
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
@ast
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
@en
type
label
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
@ast
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
@en
prefLabel
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
@ast
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
@en
P2860
P1476
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
@en
P2093
Guido Uguzzoni
Lorenzo Asti
P2860
P304
P356
10.1371/JOURNAL.PCBI.1004870
P577
2016-04-13T00:00:00Z