Lista de obras - Massimo Andreatta - Dominio Público Uruguay

An automated benchmarking platform for MHC class II binding prediction methods

artículo científico publicado en 2017

Bioinformatics Tools for the Prediction of T-Cell Epitopes

artículo científico publicado en 2018

Characterizing the binding motifs of 11 common human HLA‐DP and HLA‐DQ molecules using NNAlign ⬇️

artículo científico publicado el 1 de julio de 2012

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes

Footprints of antigen processing boost MHC class II natural ligand binding predictions

Footprints of antigen processing boost MHC class II natural ligand predictions

scholarly article by Carolina M Barra published in November 2018

Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

artículo científico publicado en 2015

GibbsCluster: unsupervised clustering and alignment of peptide sequences.

artículo científico publicado en 2017

IEDB-AR: immune epitope database-analysis resource in 2019

artículo científico publicado en 2019

Improved methods for predicting peptide binding affinity to MHC class II molecules

artículo científico publicado en 2018

In silico prediction of human pathogenicity in the γ-proteobacteria ⬇️

artículo científico publicado en 2010

MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments

scientific article published on 18 January 2019

Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

artículo científico publicado en 2017

NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.

artículo científico publicado en 2017

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data

artículo científico publicado en 2011

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

scientific article published on 02 October 2019

NetH2pan: A Computational Tool to Guide MHC peptide prediction on Murine Tumors

artículo científico publicado en 2018

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

artículo científico publicado en 2016

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

artículo científico publicado en 2017

Predicting HLA CD4 Immunogenicity in Human Populations

artículo científico publicado en 2018

Quantifying Significance of MHC II Residues.

artículo científico publicado en 2014

SPICA: Swiss portal for immune cell analysis

artículo científico publicado en 2022

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach ⬇️

artículo científico publicado el 24 de octubre de 2012

Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

artículo científico publicado en 2011

Lista de obras de Massimo Andreatta

An automated benchmarking platform for MHC class II binding prediction methods

Bioinformatics Tools for the Prediction of T-Cell Epitopes

Characterizing the binding motifs of 11 common human HLA‐DP and HLA‐DQ molecules using NNAlign ⬇️

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes

Footprints of antigen processing boost MHC class II natural ligand binding predictions

Footprints of antigen processing boost MHC class II natural ligand predictions

Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

GibbsCluster: unsupervised clustering and alignment of peptide sequences.

IEDB-AR: immune epitope database-analysis resource in 2019

Improved methods for predicting peptide binding affinity to MHC class II molecules

In silico prediction of human pathogenicity in the γ-proteobacteria ⬇️

MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments

Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

NetH2pan: A Computational Tool to Guide MHC peptide prediction on Murine Tumors

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Predicting HLA CD4 Immunogenicity in Human Populations

Quantifying Significance of MHC II Residues.

SPICA: Swiss portal for immune cell analysis

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach ⬇️

Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts