Massimo Andreatta

8.9k total citations · 7 hit papers
42 papers, 5.0k citations indexed

About

Massimo Andreatta is a scholar working on Molecular Biology, Immunology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Massimo Andreatta has authored 42 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 27 papers in Immunology and 19 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Massimo Andreatta's work include vaccines and immunoinformatics approaches (20 papers), Monoclonal and Polyclonal Antibodies Research (19 papers) and Immunotherapy and Immune Responses (14 papers). Massimo Andreatta is often cited by papers focused on vaccines and immunoinformatics approaches (20 papers), Monoclonal and Polyclonal Antibodies Research (19 papers) and Immunotherapy and Immune Responses (14 papers). Massimo Andreatta collaborates with scholars based in Denmark, Argentina and Switzerland. Massimo Andreatta's co-authors include Morten Nielsen, Santiago J. Carmona, Bjoern Peters, Paolo Marcatili, Sinu Paul, Vanessa Jurtz, Søren Buus, Alessandro Sette, Yan Zhen and Jason Greenbaum and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Massimo Andreatta

41 papers receiving 5.0k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2017 888 citations Vanessa Jurtz, Sinu Paul et al. The Journal of Immunology profile →
Gapped sequence alignment using artificial neural networks: application to the MHC class I system

2015 716 citations Massimo Andreatta, Morten Nielsen Bioinformatics profile →
Improved methods for predicting peptide binding affinity to MHC class II molecules

2018 530 citations Massimo Andreatta, Paolo Marcatili et al. profile →
UCell: Robust and scalable single-cell gene signature scoring

2021 387 citations Massimo Andreatta, Santiago J. Carmona Computational and Structural Biotechnology Journal profile →
IEDB-AR: immune epitope database—analysis resource in 2019

2019 264 citations Sandeep Kumar Dhanda, Sinu Paul et al. Nucleic Acids Research profile →
Interpretation of T cell states from single-cell transcriptomics data using reference atlases

2021 241 citations Massimo Andreatta, Jesús Corría-Osorio et al. Nature Communications profile →
IL-10-expressing CAR T cells resist dysfunction and mediate durable clearance of solid tumors and metastases

2024 144 citations Yang Zhao, Jiangqing Chen et al. Nature Biotechnology profile →

Peers

Countries citing papers authored by Massimo Andreatta

Since Specialization

Citations

This map shows the geographic impact of Massimo Andreatta's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Massimo Andreatta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Andreatta more than expected).

Fields of papers citing papers by Massimo Andreatta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Massimo Andreatta. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Massimo Andreatta. The network helps show where Massimo Andreatta may publish in the future.

Co-authorship network of co-authors of Massimo Andreatta

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Andreatta. A scholar is included among the top collaborators of Massimo Andreatta based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Massimo Andreatta. Massimo Andreatta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Age-associated nicotinamide adenine dinucleotide decline drives CAR-T cell failure 2025 ·Nature Cancer ·Helen Carrasco Hope, Jana de Sostoa, (unknown), Massimo Andreatta, (unknown), Catherine Ronet, Christine Pich-Bavastro, Jesús Corría-Osorio, François Kuonen, Johan Auwerx, Patrizia D’Amelio, Ping‐Chih Ho, Santiago J. Carmona, George Coukos, Denis Migliorini, Nicola Vannini	8
2	Perspectives on the role of “-Omics” in predicting response to immunotherapy 2025 ·European Journal of Cancer ·(unknown), Sumit K. Subudhi, Wim Vos, Massimo Andreatta, Santiago J. Carmona, (unknown), Barbara Seliger, Ramy Ibrahim, (unknown), Michael A. Smith, Alexander M.M. Eggermont, Bernard A. Fox, Michele Maio	2
3	Semi-supervised integration of single-cell transcriptomics data 2024 ·Nature Communications ·Massimo Andreatta, (unknown), Paul Gueguen, David Gfeller, Ariel Berenstein, Santiago J. Carmona	13
4	IL-10-expressing CAR T cells resist dysfunction and mediate durable clearance of solid tumors and metastases breakdown → 2024 ·Nature Biotechnology ·Yang Zhao, Jiangqing Chen, Massimo Andreatta, Bing Feng, Yuqing Xie, Mathias Wenes, Yi Wang, Min Gao, Xiaomeng Hu, Pedro Romero, Santiago J. Carmona, Jie Sun, Yugang Guo, Li Tang	144
5	Orthogonal cytokine engineering enables novel synthetic effector states escaping canonical exhaustion in tumor-rejecting CD8+ T cells 2023 ·Nature Immunology ·Jesús Corría-Osorio, Santiago J. Carmona, (unknown), Massimo Andreatta, (unknown), (unknown), Ioanna A. Rota, Isaac Crespo, Bili Seijo, Wilson Castro, Cristina Jiménez‐Luna, Léonardo Scarpellino, Catherine Ronet, Aodrenn Spill, Evripidis Lanitis, Pedro Romero, Sanjiv A. Luther, Melita Irving, George Coukos	27
6	T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps 2023 ·BIO-PROTOCOL ·Massimo Andreatta, Paul Gueguen, Nicholas Borcherding, Santiago J. Carmona	5
7	scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets 2022 ·Bioinformatics ·Massimo Andreatta, Ariel Berenstein, Santiago J. Carmona	38
8	A CD4+ T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections 2022 ·eLife ·Massimo Andreatta, (unknown), (unknown), Michael C. Kelly, Thomas Ciucci, Santiago J. Carmona	24
9	UCell: Robust and scalable single-cell gene signature scoring breakdown → 2021 ·Computational and Structural Biotechnology Journal ·Massimo Andreatta, Santiago J. Carmona	387
10	Interpretation of T cell states from single-cell transcriptomics data using reference atlases breakdown → 2021 ·Nature Communications ·Massimo Andreatta, Jesús Corría-Osorio, Sören Müller, Rafael Cubas, George Coukos, Santiago J. Carmona	241
11	STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data 2020 ·Bioinformatics ·Massimo Andreatta, Santiago J. Carmona	26
12	NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors 2018 ·Cancer Immunology Research ·(unknown), Massimo Andreatta, Wilfried Bardet, (unknown), (unknown), Kenneth W. Jackson, Alana L. Welm, Morten Nielsen, William H. Hildebrand	15
13	Footprints of antigen processing boost MHC class II natural ligand predictions 2018 ·Genome Medicine ·Carolina Barra, Bruno Alvarez, Sinu Paul, Alessandro Sette, Bjoern Peters, Massimo Andreatta, Søren Buus, Morten Nielsen	61
14	Predicting HLA CD4 Immunogenicity in Human Populations 2018 ·Frontiers in Immunology ·Sandeep Kumar Dhanda, Edita Karosiene, (unknown), Alba Grifoni, Sinu Paul, Massimo Andreatta, Daniela Weiskopf, John Sidney, Morten Nielsen, Bjoern Peters, Alessandro Sette	100
15	Unconventional Peptide Presentation by Major Histocompatibility Complex (MHC) Class I Allele HLA-A02:01 2017 ·Journal of Biological Chemistry* ·Soumya G. Remesh, Massimo Andreatta, Ying Ge, Thomas Kaever, Morten Nielsen, Curtis McMurtrey, William H. Hildebrand, Bjoern Peters, Dirk M. Zajonc	40
16	NNAlign: a platform to construct and evaluate artificial neural network models of receptor–ligand interactions 2017 ·Nucleic Acids Research ·Morten Nielsen, Massimo Andreatta	51
17	NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data breakdown → 2017 ·The Journal of Immunology ·Vanessa Jurtz, Sinu Paul, Massimo Andreatta, Paolo Marcatili, Bjoern Peters, Morten Nielsen	888
18	GibbsCluster: unsupervised clustering and alignment of peptide sequences 2017 ·Nucleic Acids Research ·Massimo Andreatta, Bruno Alvarez, Morten Nielsen	139
19	Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification 2015 ·Immunogenetics ·Massimo Andreatta, Edita Karosiene, Michael Rasmussen, Anette Stryhn, Søren Buus, Morten Nielsen	220
20	In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria 2010 ·PLoS ONE ·Massimo Andreatta, Morten Nielsen, Frank M. Aarestrup, Ole Lund	11

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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