James Bean

6.4k total citations · 1 hit paper
33 papers, 3.7k citations indexed

About

James Bean is a scholar working on Molecular Biology, Infectious Diseases and Oncology. According to data from OpenAlex, James Bean has authored 33 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 9 papers in Infectious Diseases and 8 papers in Oncology. Recurrent topics in James Bean's work include Tuberculosis Research and Epidemiology (7 papers), Mycobacterium research and diagnosis (5 papers) and Gene Regulatory Network Analysis (4 papers). James Bean is often cited by papers focused on Tuberculosis Research and Epidemiology (7 papers), Mycobacterium research and diagnosis (5 papers) and Gene Regulatory Network Analysis (4 papers). James Bean collaborates with scholars based in United States, Haiti and United Kingdom. James Bean's co-authors include Frederick R. Cross, David M. Brizel, L.R. Prosnitz, Sean P. Scully, Lester J. Layfield, John M. Harrelson, Mark W. Dewhirst, Eric D. Siggia, Daniel A. Haber and Stefano Di Talia and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

James Bean

33 papers receiving 3.6k citations

Hit Papers

Tumor oxygenation predicts for the likelihood of distant ... 1996 2026 2006 2016 1996 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Bean United States 21 2.3k 1.0k 928 643 570 33 3.7k
Kimberly R. Kalli United States 46 2.2k 1.0× 1.1k 1.1× 2.1k 2.3× 387 0.6× 638 1.1× 101 5.5k
Paul Haluska United States 38 2.5k 1.1× 1.1k 1.1× 2.0k 2.2× 618 1.0× 667 1.2× 114 4.9k
Euphemia Leung New Zealand 35 2.2k 0.9× 880 0.8× 815 0.9× 178 0.3× 385 0.7× 160 4.3k
Edward Fox United States 32 2.1k 0.9× 1.3k 1.2× 802 0.9× 299 0.5× 512 0.9× 94 4.1k
Devalingam Mahalingam United States 41 2.9k 1.3× 922 0.9× 2.0k 2.2× 711 1.1× 515 0.9× 186 5.4k
Salvatore Venuta Italy 45 2.2k 1.0× 757 0.7× 1.6k 1.7× 276 0.4× 606 1.1× 137 5.1k
Ravid Straussman Israel 20 3.7k 1.6× 726 0.7× 1.8k 2.0× 511 0.8× 358 0.6× 28 5.3k
Henrik J. Ditzel Denmark 48 3.7k 1.6× 1.6k 1.5× 1.9k 2.1× 800 1.2× 583 1.0× 214 7.1k
Jan Theys Netherlands 36 1.3k 0.6× 766 0.7× 679 0.7× 333 0.5× 656 1.2× 98 3.1k
Stanley B. Kaye United Kingdom 35 3.2k 1.4× 949 0.9× 2.5k 2.7× 627 1.0× 1.2k 2.1× 102 6.1k

Countries citing papers authored by James Bean

Since Specialization
Citations

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

Fields of papers citing papers by James Bean

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Bean

This figure shows the co-authorship network connecting the top 25 collaborators of James Bean. A scholar is included among the top collaborators of James Bean 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 James Bean. James Bean 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.
Rosati, Barbara, David Carlson, Nai‐Kong V. Cheung, et al.. (2025). CD8α marks a Mycobacterium tuberculosis-reactive human NK cell population with high activation potential. Scientific Reports. 15(1). 15095–15095. 1 indexed citations
3.
Ginn, John D., Tomasz Kochańczyk, Xiuju Jiang, et al.. (2024). Indazole to 2‐Cyanoindole Scaffold Progression for Mycobacterial Lipoamide Dehydrogenase Inhibitors Achieves Extended Target Residence Time and Improved Antibacterial Activity. Angewandte Chemie International Edition. 63(44). e202407276–e202407276. 3 indexed citations
4.
Qu, Di, Peng Ge, Laure Botella, et al.. (2024). Mycobacterial biotin synthases require an auxiliary protein to convert dethiobiotin into biotin. Nature Communications. 15(1). 4161–4161. 3 indexed citations
5.
Redelman‐Sidi, Gil, et al.. (2022). BCG-Induced Tumor Immunity Requires Tumor-Intrinsic CIITA Independent of MHC-II. Cancer Immunology Research. 10(10). 1241–1253. 5 indexed citations
6.
Dupuy, Pierre, et al.. (2021). Division of labor between SOS and PafBC in mycobacterial DNA repair and mutagenesis. Nucleic Acids Research. 49(22). 12805–12819. 20 indexed citations
8.
Lee, Myung Hee, Kathleen F. Walsh, James Bean, et al.. (2020). Urinary biomarkers of mycobacterial load and treatment response in pulmonary tuberculosis. JCI Insight. 5(18). 7 indexed citations
9.
Bockman, Matthew R., Curtis A. Engelhart, Neeraj K. Mishra, et al.. (2019). Investigation of (S)-(−)-Acidomycin: A Selective Antimycobacterial Natural Product That Inhibits Biotin Synthase. ACS Infectious Diseases. 5(4). 598–617. 26 indexed citations
10.
Isa, Flonza, Sean Collins, Myung Hee Lee, et al.. (2018). Mass Spectrometric Identification of Urinary Biomarkers of Pulmonary Tuberculosis. EBioMedicine. 31. 157–165. 46 indexed citations
11.
Vorkas, Charles Kyriakos, Matthew F. Wipperman, Kelin Li, et al.. (2018). Mucosal-associated invariant and γδ T cell subsets respond to initial Mycobacterium tuberculosis infection. JCI Insight. 3(19). 50 indexed citations
12.
Wipperman, Matthew F., Daniel W. Fitzgerald, Marc Antoine Jean Juste, et al.. (2017). Antibiotic treatment for Tuberculosis induces a profound dysbiosis of the microbiome that persists long after therapy is completed. Scientific Reports. 7(1). 10767–10767. 133 indexed citations
13.
Ullman, Erica, et al.. (2014). Altered Transcriptional Control Networks with Trans-Differentiation of Isogenic Mutant-KRas NSCLC Models. Frontiers in Oncology. 4. 344–344. 16 indexed citations
14.
Thomson, Stuart, Filippo Petti, Peter Mercado, et al.. (2010). A systems view of epithelial–mesenchymal transition signaling states. Clinical & Experimental Metastasis. 28(2). 137–155. 178 indexed citations
15.
Talia, Stefano Di, Jan M. Skotheim, James Bean, Eric D. Siggia, & Frederick R. Cross. (2007). The effects of molecular noise and size control on variability in the budding yeast cell cycle. Nature. 448(7156). 947–951. 356 indexed citations
16.
Bean, James, Eric D. Siggia, & Frederick R. Cross. (2006). Coherence and Timing of Cell Cycle Start Examined at Single-Cell Resolution. Molecular Cell. 21(1). 3–14. 100 indexed citations
17.
Bernstein, Kara A., Franziska Bleichert, James Bean, Frederick R. Cross, & Susan J. Baserga. (2006). Ribosome Biogenesis Is Sensed at the Start Cell Cycle Checkpoint. Molecular Biology of the Cell. 18(3). 953–964. 110 indexed citations
18.
Harkin, D. Paul, James Bean, David B. Miklos, et al.. (1999). Induction of GADD45 and JNK/SAPK-Dependent Apoptosis following Inducible Expression of BRCA1. Cell. 97(5). 575–586. 480 indexed citations
19.
Maheswaran, Shyamala, Christoph Englert, Gang Zheng, et al.. (1998). Inhibition of cellular proliferation by the Wilms tumor suppressor WT1 requires association with the inducible chaperone Hsp70. Genes & Development. 12(8). 1108–1120. 82 indexed citations
20.
FitzGerald, Michael G., James Bean, Sanjay R. Hegde, et al.. (1997). Heterozygous ATM mutations do not contribute to early onset of breast cancer. Nature Genetics. 15(3). 307–310. 254 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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