Prediction of MHC class I binding peptides using probability distribution functions.

about

Residue analysis of a CTL epitope of SARS-CoV spike protein by IFN-gamma production and bioinformatics prediction.

P2860

Q34410310-BEA1B512-3913-42AC-8C98-98BE6F9D5A51

P2860

Prediction of MHC class I binding peptides using probability distribution functions.

Item

http://www.wikidata.org/entity/Q42066174

description

2009 nî lūn-bûn

@nan

2009年の論文

@ja

2009年論文

@yue

2009年論文

@zh-hant

2009年論文

@zh-hk

2009年論文

@zh-mo

2009年論文

@zh-tw

2009年论文

@wuu

2009年论文

@zh

2009年论文

@zh-cn

name

Prediction of MHC class I binding peptides using probability distribution functions.

@en

Prediction of MHC class I binding peptides using probability distribution functions.

@nl

type

Item

label

Prediction of MHC class I binding peptides using probability distribution functions.

@en

Prediction of MHC class I binding peptides using probability distribution functions.

@nl

prefLabel

Prediction of MHC class I binding peptides using probability distribution functions.

@en

Prediction of MHC class I binding peptides using probability distribution functions.

@nl

P1476

Prediction of MHC class I binding peptides using probability distribution functions

@en

P2093

Sudhir Singh Soam

http://www.w3.org/2001/XMLSchema#string

P2860

MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.

SYFPEITHI: database for MHC ligands and peptide motifs

A hybrid genetic-neural system for predicting protein secondary structure.

Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.

Immuno-informatics: Mining genomes for vaccine components.

PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing.

Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications.

Prediction of CTL epitopes using QM, SVM and ANN techniques.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network.

A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes.

P304

403-408

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.6026/97320630003403

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Feroz Khan

Bharat Bhasker

Bhartendu Nath Mishra

P577

2009-06-28T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

26817468

http://www.w3.org/2001/XMLSchema#string

P698

19759816

http://www.w3.org/2001/XMLSchema#string

P932

2732036

http://www.w3.org/2001/XMLSchema#string

Prediction of MHC class I binding peptides using probability distribution functions.

about

P2860

P2860

Prediction of MHC class I binding peptides using probability distribution functions.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577

P5875

P698

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577

P5875

P698

P932