Thomas MacCarthy

1.4k total citations
50 papers, 900 citations indexed

About

Thomas MacCarthy is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Thomas MacCarthy has authored 50 papers receiving a total of 900 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 17 papers in Immunology and 10 papers in Genetics. Recurrent topics in Thomas MacCarthy's work include T-cell and B-cell Immunology (12 papers), Monoclonal and Polyclonal Antibodies Research (7 papers) and Chronic Lymphocytic Leukemia Research (6 papers). Thomas MacCarthy is often cited by papers focused on T-cell and B-cell Immunology (12 papers), Monoclonal and Polyclonal Antibodies Research (7 papers) and Chronic Lymphocytic Leukemia Research (6 papers). Thomas MacCarthy collaborates with scholars based in United States, United Kingdom and Italy. Thomas MacCarthy's co-authors include Aviv Bergman, Matthew D. Scharff, Sergio Roa, Jeffrey Chen, Kenny Ye, Gil Atzmon, Nir Barzilai, Catherine Tang, Robert M. Seymour and Andrew Pomiankowski and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and The Journal of Experimental Medicine.

In The Last Decade

Thomas MacCarthy

49 papers receiving 891 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas MacCarthy United States 18 505 286 151 111 106 50 900
Sébastien Storck France 16 504 1.0× 650 2.3× 66 0.4× 125 1.1× 69 0.7× 19 1.2k
Ted A. Torrey United States 16 538 1.1× 294 1.0× 221 1.5× 235 2.1× 73 0.7× 22 996
Vasco M. Barreto Portugal 14 605 1.2× 909 3.2× 95 0.6× 154 1.4× 69 0.7× 24 1.3k
Brian Ondek United States 5 451 0.9× 193 0.7× 116 0.8× 177 1.6× 56 0.5× 8 734
Tetyana Klymenko United Kingdom 14 1.1k 2.1× 114 0.4× 129 0.9× 141 1.3× 83 0.8× 20 1.4k
Sofia Kossida France 14 367 0.7× 367 1.3× 44 0.3× 92 0.8× 88 0.8× 35 757
Gabriele Beck‐Engeser United States 19 465 0.9× 563 2.0× 130 0.9× 151 1.4× 44 0.4× 31 1.0k
Andrew Peters United States 8 421 0.8× 562 2.0× 65 0.4× 158 1.4× 190 1.8× 12 1.0k
Nilushi S. De Silva United States 10 417 0.8× 1.0k 3.6× 62 0.4× 203 1.8× 88 0.8× 12 1.4k
Lori R. Covey United States 21 436 0.9× 855 3.0× 124 0.8× 275 2.5× 67 0.6× 43 1.3k

Countries citing papers authored by Thomas MacCarthy

Since Specialization
Citations

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

Fields of papers citing papers by Thomas MacCarthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas MacCarthy

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

All Works

20 of 20 papers shown
1.
Galbo, Phillip M., Kartik Chandran, Yinghao Wu, et al.. (2024). Fine-tuning spatial-temporal dynamics and surface receptor expression support plasma cell-intrinsic longevity. eLife. 12. 2 indexed citations
2.
3.
Hu, Changkun, Rachel Palinski, Ibukun A. Akinyemi, et al.. (2023). Beta human papillomavirus 8E6 promotes alternative end joining. eLife. 12. 7 indexed citations
4.
Galbo, Phillip M., Kartik Chandran, Yinghao Wu, et al.. (2023). Fine-tuning spatial-temporal dynamics and surface receptor expression support plasma cell-intrinsic longevity. eLife. 12. 7 indexed citations
5.
Wang, Shanzhi, Stephen Gray, Yongwei Zhang, et al.. (2022). Role of EXO1 nuclease activity in genome maintenance, the immune response and tumor suppression in Exo1D173A mice. Nucleic Acids Research. 50(14). 8093–8106. 12 indexed citations
6.
7.
Tang, Catherine, et al.. (2021). Correlations in Somatic Hypermutation Between Sites in IGHV Genes Can Be Explained by Interactions Between AID and/or Polη Hotspots. Frontiers in Immunology. 11. 618409–618409. 3 indexed citations
8.
Bagnara, Davide, Catherine Tang, Jennifer R. Brown, et al.. (2021). Post-Transformation IGHV-IGHD-IGHJ Mutations in Chronic Lymphocytic Leukemia B Cells: Implications for Mutational Mechanisms and Impact on Clinical Course. Frontiers in Oncology. 11. 640731–640731. 8 indexed citations
9.
Yu, Guojun, Yingru Wu, Catherine Tang, et al.. (2021). A Bayesian model based computational analysis of the relationship between bisulfite accessible single-stranded DNA in chromatin and somatic hypermutation of immunoglobulin genes. PLoS Computational Biology. 17(9). e1009323–e1009323. 2 indexed citations
10.
Tang, Catherine, Davide Bagnara, Nicholas Chiorazzi, Matthew D. Scharff, & Thomas MacCarthy. (2020). AID Overlapping and Polη Hotspots Are Key Features of Evolutionary Variation Within the Human Antibody Heavy Chain (IGHV) Genes. Frontiers in Immunology. 11. 788–788. 21 indexed citations
11.
12.
MacCarthy, Thomas, et al.. (2018). The cytidine deaminase under-representation reporter (CDUR) as a tool to study evolution of sequences under deaminase mutational pressure. BMC Bioinformatics. 19(1). 163–163. 7 indexed citations
13.
14.
Maul, Robert W., Thomas MacCarthy, Ekaterina G. Frank, et al.. (2016). DNA polymerase ι functions in the generation of tandem mutations during somatic hypermutation of antibody genes. The Journal of Experimental Medicine. 213(9). 1675–1683. 23 indexed citations
15.
Patten, Piers, Gerardo Ferrer, Shih‐Shih Chen, et al.. (2016). Chronic lymphocytic leukemia cells diversify and differentiate in vivo via a nonclassical Th1-dependent, Bcl-6–deficient process. JCI Insight. 1(4). 28 indexed citations
16.
Gombar, Saurabh, Thomas MacCarthy, & Aviv Bergman. (2014). Epigenetics Decouples Mutational from Environmental Robustness. Did It Also Facilitate Multicellularity?. PLoS Computational Biology. 10(3). e1003450–e1003450. 14 indexed citations
17.
Roa, Sergio, Elena Avdievich, Jonathan U. Peled, et al.. (2008). Ubiquitylated PCNA plays a role in somatic hypermutation and class-switch recombination and is required for meiotic progression. Proceedings of the National Academy of Sciences. 105(42). 16248–16253. 94 indexed citations
18.
MacCarthy, Thomas, Sergio Roa, Matthew D. Scharff, & Aviv Bergman. (2008). SHMTool: A webserver for comparative analysis of somatic hypermutation datasets. DNA repair. 8(1). 137–141. 33 indexed citations
19.
MacCarthy, Thomas & Aviv Bergman. (2007). The limits of subfunctionalization. BMC Evolutionary Biology. 7(1). 213–213. 41 indexed citations
20.
Bergman, Aviv, Gil Atzmon, Kenny Ye, Thomas MacCarthy, & Nir Barzilai. (2007). Buffering Mechanisms in Aging: A Systems Approach Toward Uncovering the Genetic Component of Aging. PLoS Computational Biology. 3(8). e170–e170. 88 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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