William W. Hannon

2.1k total citations · 3 hit papers
12 papers, 929 citations indexed

About

William W. Hannon is a scholar working on Molecular Biology, Infectious Diseases and Epidemiology. According to data from OpenAlex, William W. Hannon has authored 12 papers receiving a total of 929 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 6 papers in Infectious Diseases and 3 papers in Epidemiology. Recurrent topics in William W. Hannon's work include SARS-CoV-2 and COVID-19 Research (5 papers), vaccines and immunoinformatics approaches (3 papers) and Influenza Virus Research Studies (2 papers). William W. Hannon is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (5 papers), vaccines and immunoinformatics approaches (3 papers) and Influenza Virus Research Studies (2 papers). William W. Hannon collaborates with scholars based in United States, Switzerland and United Kingdom. William W. Hannon's co-authors include Jesse D. Bloom, Allison J. Greaney, Tyler N. Starr, Amin Addetia, Jonathan Z. Li, Adam S. Dingens, Manish C. Choudhary, David Veesler, Bernadeta Dadonaite and Ariana Ghez Farrell and has published in prestigious journals such as Science, Cell and Molecular Cell.

In The Last Decade

William W. Hannon

12 papers receiving 921 citations

Hit Papers

Prospective mapping of viral mutations that escape antibo... 2021 2026 2022 2024 2021 2022 2023 100 200 300 400
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. Prospective mapping of viral mutations that escape antibodies used to treat COVID-19
    2021 448 citations Tyler N. Starr, Allison J. Greaney et al. Science profile →
  2. Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution
    2022 157 citations Tyler N. Starr, Allison J. Greaney et al. Science profile →
  3. A pseudovirus system enables deep mutational scanning of the full SARS-CoV-2 spike
    2023 85 citations Bernadeta Dadonaite, Katharine H. D. Crawford et al. Cell profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William W. Hannon United States 10 684 441 142 97 88 12 929
Rachel S. Nargi United States 10 840 1.2× 316 0.7× 171 1.2× 131 1.4× 132 1.5× 15 997
Keara D. Malone United States 5 1.1k 1.6× 382 0.9× 130 0.9× 76 0.8× 169 1.9× 5 1.2k
Mary Jane Navarro United States 4 1.1k 1.5× 544 1.2× 130 0.9× 60 0.6× 165 1.9× 4 1.3k
Claudia A. Jette United States 6 826 1.2× 360 0.8× 200 1.4× 59 0.6× 128 1.5× 8 977
Kim-Marie A. Dam United States 7 844 1.2× 342 0.8× 225 1.6× 84 0.9× 121 1.4× 11 1.0k
Daniela Fera United States 8 623 0.9× 248 0.6× 144 1.0× 81 0.8× 65 0.7× 9 868
Katarzyna Janowska United States 8 690 1.0× 316 0.7× 75 0.5× 59 0.6× 126 1.4× 15 807
Cameron Stewart United States 9 535 0.8× 234 0.5× 63 0.4× 48 0.5× 90 1.0× 21 642
Stephanie Giordano United States 3 650 1.0× 203 0.5× 126 0.9× 46 0.5× 99 1.1× 4 737

Countries citing papers authored by William W. Hannon

Since Specialization
Citations

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

Fields of papers citing papers by William W. Hannon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William W. Hannon. 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 William W. Hannon. The network helps show where William W. Hannon may publish in the future.

Co-authorship network of co-authors of William W. Hannon

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

All Works

12 of 12 papers shown
1.
Dadonaite, Bernadeta, Yu Jin, Colleen Furey, et al.. (2024). Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance. PLoS Biology. 22(11). e3002916–e3002916. 22 indexed citations
2.
Loes, Andrea N., John Huddleston, Sook‐San Wong, et al.. (2024). High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains. Journal of Virology. 98(10). e0068924–e0068924. 10 indexed citations
3.
Hannon, William W. & Jesse D. Bloom. (2024). dms-viz: Structure-informed visualizations for deepmutational scanning and other mutation-based datasets. The Journal of Open Source Software. 9(99). 6129–6129. 5 indexed citations
4.
Hannon, William W., Ryan C. Donohue, Christian K. Pfaller, et al.. (2023). Brain tropism acquisition: The spatial dynamics and evolution of a measles virus collective infectious unit that drove lethal subacute sclerosing panencephalitis. PLoS Pathogens. 19(12). e1011817–e1011817. 11 indexed citations
5.
Dadonaite, Bernadeta, Katharine H. D. Crawford, Caelan E. Radford, et al.. (2023). A pseudovirus system enables deep mutational scanning of the full SARS-CoV-2 spike. Cell. 186(6). 1263–1278.e20. 85 indexed citations breakdown →
6.
Yu, Timothy C., William W. Hannon, William S. DeWitt, et al.. (2022). A biophysical model of viral escape from polyclonal antibodies. Virus Evolution. 8(2). veac110–veac110. 19 indexed citations
7.
Hannon, William W., Pavitra Roychoudhury, Hong Xie, et al.. (2022). Narrow transmission bottlenecks and limited within-host viral diversity during a SARS-CoV-2 outbreak on a fishing boat. Virus Evolution. 8(2). veac052–veac052. 10 indexed citations
8.
Starr, Tyler N., Allison J. Greaney, William W. Hannon, et al.. (2022). Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science. 377(6604). 420–424. 157 indexed citations breakdown →
9.
Zhou, Meng, Minjeong Ko, William W. Hannon, et al.. (2022). Patterns of structural variation define prostate cancer across disease states. JCI Insight. 7(17). 9 indexed citations
10.
Starr, Tyler N., Allison J. Greaney, Cameron Stewart, et al.. (2022). Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains. PLoS Pathogens. 18(11). e1010951–e1010951. 85 indexed citations
11.
Starr, Tyler N., Allison J. Greaney, Amin Addetia, et al.. (2021). Prospective mapping of viral mutations that escape antibodies used to treat COVID-19. Science. 371(6531). 850–854. 448 indexed citations breakdown →
12.
Kim, Jeongkyu, David Sturgill, Robin Sebastian, et al.. (2017). Replication Stress Shapes a Protective Chromatin Environment across Fragile Genomic Regions. Molecular Cell. 69(1). 36–47.e7. 68 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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