Massimo Andreatta

Publications

• NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T cell epitope predictions.
• STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data
• Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses
• STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data
• Immunoinformatics: Predicting Peptide–MHC Binding
• UCell: robust and scalable single-cell gene signature scoring
• Interpretation of T cell states from single-cell transcriptomics data using reference atlases
• Low Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• An automated benchmarking platform for MHC class II binding prediction methods
• Improved methods for predicting peptide binding affinity to MHC class II molecules
• Footprints of antigen processing boost MHC class II natural ligand binding predictions
• NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors
• MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments
• IEDB-AR: immune epitope database—analysis resource in 2019
• Bioinformatics Tools for the Prediction of T-Cell Epitopes.
• Footprints of antigen processing boost MHC class II natural ligand predictions.
• Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes.
• Predicting HLA CD4 immunogenicity in human populations
• Gapped sequence alignment using artificial neural networks: application to the MHC class I system.
• Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.
• NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.
• Breaking confinement: unconventional peptide presentation by major histocompatibility (MHC) class I allele HLA-A*02:01.
• NNAlign: a platform to construct and evaluate artificial neural network models of receptor–ligand interactions
• GibbsCluster: unsupervised clustering and alignment of peptide sequences
• Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules
• NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
• Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes
• Gapped sequence alignment using artificial neural networks: Application to the MHC class i system
• In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria
• Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlign
• Quantifying Significance of MHC II Residues
• NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data
• Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts
• Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach.
• Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification
• SPICA: Swiss portal for immune cell analysis
• scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets
• Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Orthogonal Gene Engineering Enables CD8+ T Cells to Control Tumors through a Novel PD-1+ TOX-indifferent Synthetic Effector State
• Orthogonal Gene Engineering Enables CD8+ T Cells to Control Tumors through a Novel PD-1+TOX-indifferent Synthetic Effector State
• NFAT5 induction by the tumor microenvironment enforces CD8 T cell exhaustion
• Dissecting the treatment-naive ecosystem of human melanoma brain metastasis
• A CD4+ T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Data from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Data from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Supplementary Data from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Data from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy
• Data from NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors
• Supplemental tables and figures from NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors
• Orthogonal cytokine engineering enables novel synthetic effector states escaping canonical exhaustion in tumor-rejecting CD8+ T cells
• Semi-supervised integration of single-cell transcriptomics data
• Activation of the transcription factor NFAT5 in the tumor microenvironment enforces CD8+ T cell exhaustion
• 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
• IL-10-expressing CAR T cells resist dysfunction and mediate durable clearance of solid tumors and metastases
• Wounding triggers invasive progression in human basal cell carcinoma
• ECM Signatures Reveal Quiescent Stem Cell Diversity in the Colonic Niche
• Metabolic reinvigoration of NK cells by IL-21 enhances immunotherapy against MHC-I deficient solid tumors
• Perspectives on the Role of “-Omics” in Predicting Response to Immunotherapy
• Perspectives on the role of “-Omics” in predicting response to immunotherapy