Brian Long

28 total papers · 791 total citations
22 papers, 451 citations indexed

About

Brian Long is a scholar working on Genetics, Oncology and Molecular Biology. According to data from OpenAlex, Brian Long has authored 22 papers receiving a total of 451 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Genetics, 15 papers in Oncology and 11 papers in Molecular Biology. Recurrent topics in Brian Long's work include Virus-based gene therapy research (17 papers), CAR-T cell therapy research (15 papers) and Viral Infectious Diseases and Gene Expression in Insects (6 papers). Brian Long is often cited by papers focused on Virus-based gene therapy research (17 papers), CAR-T cell therapy research (15 papers) and Viral Infectious Diseases and Gene Expression in Insects (6 papers). Brian Long collaborates with scholars based in United States, France and United Kingdom. Brian Long's co-authors include Christian Vettermann, Stephen J. Zoog, Becky Schweighardt, Laurie Tsuruda, Sylvia Fong, Charles O’Neill, Boris Gorovits, Mark Milton, Gregory M. Hayes and Jennifer Holcomb and has published in prestigious journals such as Blood, The Journal of Immunology and Journal of Animal Ecology.

In The Last Decade

Brian Long

21 papers receiving 436 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Brian Long 297 214 183 65 64 22 451
Romain Hardet 293 1.0× 263 1.2× 145 0.8× 56 0.9× 62 1.0× 14 442
Janik Adriaansen 248 0.8× 206 1.0× 101 0.6× 90 1.4× 145 2.3× 19 456
Thomas B. Okarma 187 0.6× 234 1.1× 91 0.5× 139 2.1× 25 0.4× 21 527
Jean-François Dedieu 221 0.7× 293 1.4× 173 0.9× 42 0.6× 21 0.3× 15 485
Elisa Masat 215 0.7× 232 1.1× 85 0.5× 92 1.4× 52 0.8× 12 460
Susannah I. Thornhill 256 0.9× 281 1.3× 131 0.7× 144 2.2× 16 0.3× 14 500
Michael L. Kaufman 235 0.8× 372 1.7× 146 0.8× 93 1.4× 12 0.2× 21 520
Victoria Bordon 220 0.7× 184 0.9× 153 0.8× 165 2.5× 14 0.2× 32 520
Markus D. Lacher 125 0.4× 286 1.3× 143 0.8× 56 0.9× 20 0.3× 25 411
Kavita Desai 101 0.3× 159 0.7× 143 0.8× 46 0.7× 41 0.6× 22 458

Countries citing papers authored by Brian Long

Since Specialization
Citations

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

Fields of papers citing papers by Brian Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brian Long

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

All Works

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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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