Gabriele Pozzati

2.7k total citations · 2 hit papers
5 papers, 842 citations indexed

About

Gabriele Pozzati is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Gabriele Pozzati has authored 5 papers receiving a total of 842 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 2 papers in Materials Chemistry. Recurrent topics in Gabriele Pozzati's work include Protein Structure and Dynamics (4 papers), Machine Learning in Bioinformatics (3 papers) and RNA and protein synthesis mechanisms (2 papers). Gabriele Pozzati is often cited by papers focused on Protein Structure and Dynamics (4 papers), Machine Learning in Bioinformatics (3 papers) and RNA and protein synthesis mechanisms (2 papers). Gabriele Pozzati collaborates with scholars based in Sweden, United States and Netherlands. Gabriele Pozzati's co-authors include Arne Elofsson, Patrick Bryant, Petras J. Kundrotas, Wensi Zhu, Aditi Shenoy, David F. Burke, Alistair S. Dunham, James E. Bruce, Inigo Barrio‐Hernandez and Pascal Albanese and has published in prestigious journals such as Nature Communications, Bioinformatics and Nature Structural & Molecular Biology.

In The Last Decade

Gabriele Pozzati

5 papers receiving 831 citations

Hit Papers

Improved prediction of protein-protein interactions using... 2022 2026 2023 2024 2022 2023 100 200 300 400 500
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.

  1. Improved prediction of protein-protein interactions using AlphaFold2
    2022 557 citations Patrick Bryant, Gabriele Pozzati et al. Nature Communications profile →
  2. Towards a structurally resolved human protein interaction network
    2023 134 citations David F. Burke, Patrick Bryant et al. Nature Structural & Molecular Biology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gabriele Pozzati Sweden 5 722 168 115 51 45 5 842
Gyu Rie Lee South Korea 14 748 1.0× 148 0.9× 122 1.1× 99 1.9× 34 0.8× 23 909
Alexander Miguel Monzón Italy 17 970 1.3× 253 1.5× 116 1.0× 33 0.6× 73 1.6× 43 1.1k
Jianzhao Gao China 16 750 1.0× 150 0.9× 146 1.3× 109 2.1× 29 0.6× 35 875
Lane Votapka United States 13 614 0.9× 84 0.5× 179 1.6× 66 1.3× 45 1.0× 25 784
Jon E. Black Netherlands 3 561 0.8× 166 1.0× 63 0.5× 30 0.6× 40 0.9× 4 709
Noelia Ferruz Spain 12 652 0.9× 131 0.8× 117 1.0× 68 1.3× 35 0.8× 20 818
David La United States 14 571 0.8× 169 1.0× 131 1.1× 67 1.3× 45 1.0× 19 720
Justas Dauparas United States 14 945 1.3× 193 1.1× 95 0.8× 87 1.7× 47 1.0× 20 1.2k
Sankar Basu India 15 614 0.9× 194 1.2× 147 1.3× 59 1.2× 23 0.5× 39 791
Yuichiro Hourai Japan 5 439 0.6× 82 0.5× 93 0.8× 62 1.2× 33 0.7× 6 570

Countries citing papers authored by Gabriele Pozzati

Since Specialization
Citations

This map shows the geographic impact of Gabriele Pozzati'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 Gabriele Pozzati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriele Pozzati more than expected).

Fields of papers citing papers by Gabriele Pozzati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriele Pozzati. 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 Gabriele Pozzati. The network helps show where Gabriele Pozzati may publish in the future.

Co-authorship network of co-authors of Gabriele Pozzati

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriele Pozzati. A scholar is included among the top collaborators of Gabriele Pozzati 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 Gabriele Pozzati. Gabriele Pozzati is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
1.
Burke, David F., Patrick Bryant, Inigo Barrio‐Hernandez, et al.. (2023). Towards a structurally resolved human protein interaction network. Nature Structural & Molecular Biology. 30(2). 216–225. 134 indexed citations breakdown →
2.
Bryant, Patrick, Gabriele Pozzati, & Arne Elofsson. (2022). Improved prediction of protein-protein interactions using AlphaFold2. Nature Communications. 13(1). 1265–1265. 557 indexed citations breakdown →
3.
Bryant, Patrick, Gabriele Pozzati, Wensi Zhu, et al.. (2022). Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search. Nature Communications. 13(1). 6028–6028. 128 indexed citations
4.
Pozzati, Gabriele, Petras J. Kundrotas, & Arne Elofsson. (2022). Scoring of protein–protein docking models utilizing predicted interface residues. Proteins Structure Function and Bioinformatics. 90(7). 1493–1505. 7 indexed citations
5.
Pozzati, Gabriele, Wensi Zhu, Claudio Bassot, et al.. (2021). Limits and potential of combined folding and docking. Bioinformatics. 38(4). 954–961. 16 indexed citations

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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