List of works - Massimo Andreatta

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

scientific article

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

scientific article published on 4 October 2017

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

scientific article published on March 30, 2016

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

scientific article (publication date: 25 August 2011)

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

scientific article published on 6 January 2018

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

scientific article published on October 24, 2012

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

scientific article (publication date: 2011)

GibbsCluster: unsupervised clustering and alignment of peptide sequences.

scientific article published on 12 April 2017

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

scientific article published on 01 July 2019

An automated benchmarking platform for MHC class II binding prediction methods

scientific article

Footprints of antigen processing boost MHC class II natural ligand predictions

scholarly article by Carolina M Barra published in November 2018

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

scientific article published in July 2017

Predicting HLA CD4 Immunogenicity in Human Populations

scientific article

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

scientific article published on July 1, 2012

In silico prediction of human pathogenicity in the γ-proteobacteria

scientific article

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

scientific article published on 24 May 2017

Bioinformatics Tools for the Prediction of T-Cell Epitopes

scientific article published on 01 January 2018

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

scientific article published on 02 October 2019

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

scientific article published on 18 January 2019

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

scientific article published on 3 April 2018

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

SPICA: Swiss portal for immune cell analysis

scientific article published on 01 January 2022

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

Quantifying Significance of MHC II Residues.

scientific article published on January 2014

List of works by Massimo Andreatta

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

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

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

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

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

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

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

GibbsCluster: unsupervised clustering and alignment of peptide sequences.

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

An automated benchmarking platform for MHC class II binding prediction methods

Footprints of antigen processing boost MHC class II natural ligand predictions

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

Predicting HLA CD4 Immunogenicity in Human Populations

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

In silico prediction of human pathogenicity in the γ-proteobacteria

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

Bioinformatics Tools for the Prediction of T-Cell Epitopes

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

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

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

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

SPICA: Swiss portal for immune cell analysis

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

Quantifying Significance of MHC II Residues.