Roy A. Black

18.1k total citations · 7 hit papers
95 papers, 14.8k citations indexed

About

Roy A. Black is a scholar working on Molecular Biology, Immunology and Allergy and Cancer Research. According to data from OpenAlex, Roy A. Black has authored 95 papers receiving a total of 14.8k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Molecular Biology, 28 papers in Immunology and Allergy and 25 papers in Cancer Research. Recurrent topics in Roy A. Black's work include Cell Adhesion Molecules Research (27 papers), Protease and Inhibitor Mechanisms (17 papers) and Origins and Evolution of Life (15 papers). Roy A. Black is often cited by papers focused on Cell Adhesion Molecules Research (27 papers), Protease and Inhibitor Mechanisms (17 papers) and Origins and Evolution of Life (15 papers). Roy A. Black collaborates with scholars based in United States, Canada and Japan. Roy A. Black's co-authors include Douglas Pat Cerretti, Carl J. March, Jacques J. Peschon, С. Kronheim, Carl J. Kozlosky, Beverly J. Castner, Jennifer L. Slack, Nicole Nelson, Richard S. Johnson and Raymond J. Paxton and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Roy A. Black

93 papers receiving 14.5k citations

Hit Papers

A metalloproteinase disintegrin that releases tumour-necr... 1992 2026 2003 2014 1997 1998 1992 2000 1992 500 1000 1.5k 2.0k 2.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roy A. Black United States 50 7.3k 3.9k 3.8k 2.7k 2.6k 95 14.8k
Douglas Pat Cerretti United States 47 7.1k 1.0× 4.1k 1.0× 3.0k 0.8× 1.9k 0.7× 1.7k 0.6× 82 14.5k
Carl Blobel United States 72 8.0k 1.1× 2.8k 0.7× 5.6k 1.5× 4.4k 1.6× 2.7k 1.0× 148 16.2k
Charles T. Rauch United States 24 8.2k 1.1× 5.3k 1.4× 3.2k 0.9× 1.4k 0.5× 2.6k 1.0× 33 14.1k
Mathew A. Vadas Australia 69 9.2k 1.3× 4.4k 1.1× 2.2k 0.6× 1.9k 0.7× 3.7k 1.4× 207 17.4k
Carl J. March United States 38 6.5k 0.9× 6.6k 1.7× 3.2k 0.8× 1.9k 0.7× 1.7k 0.7× 60 15.2k
Tucker Collins United States 55 6.0k 0.8× 4.0k 1.0× 1.5k 0.4× 2.0k 0.7× 2.5k 0.9× 86 12.4k
Carlos Martı́nez-A Spain 81 8.6k 1.2× 9.4k 2.4× 5.3k 1.4× 2.1k 0.8× 1.5k 0.6× 331 20.5k
Chikao Morimoto United States 74 5.1k 0.7× 8.4k 2.2× 5.6k 1.5× 2.7k 1.0× 1.7k 0.6× 339 18.5k
Emilio Hirsch Italy 73 9.6k 1.3× 5.4k 1.4× 2.9k 0.8× 1.8k 0.6× 1.5k 0.6× 297 19.0k
Martin Turner United Kingdom 67 7.8k 1.1× 8.6k 2.2× 2.7k 0.7× 1.6k 0.6× 3.7k 1.4× 208 17.6k

Countries citing papers authored by Roy A. Black

Since Specialization
Citations

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

Fields of papers citing papers by Roy A. Black

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roy A. Black. 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 Roy A. Black. The network helps show where Roy A. Black may publish in the future.

Co-authorship network of co-authors of Roy A. Black

This figure shows the co-authorship network connecting the top 25 collaborators of Roy A. Black. A scholar is included among the top collaborators of Roy A. Black 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 Roy A. Black. Roy A. Black 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.
Ding, Dian, Lijun Zhou, Saurja DasGupta, et al.. (2024). Natural soda lakes provide compatible conditions for RNA and membrane function that could have enabled the origin of life. PNAS Nexus. 3(3). pgae084–pgae084. 4 indexed citations
3.
Todd, Zoe R., et al.. (2022). Plausible Sources of Membrane-Forming Fatty Acids on the Early Earth: A Review of the Literature and an Estimation of Amounts. ACS Earth and Space Chemistry. 7(1). 11–27. 15 indexed citations
5.
Todd, Zoe R., et al.. (2022). Prebiotic Vesicles Retain Solutes and Grow by Micelle Addition after Brief Cooling below the Membrane Melting Temperature. Langmuir. 38(44). 13407–13413. 1 indexed citations
6.
7.
Hazra, Avijit, Julia Nguyen, Richard S. Johnson, et al.. (2021). Prebiotic Membranes and Micelles Do Not Inhibit Peptide Formation During Dehydration. ChemBioChem. 23(3). e202100614–e202100614. 4 indexed citations
8.
Xue, Mengjun, et al.. (2021). Binding of Dipeptides to Fatty Acid Membranes Explains Their Colocalization in Protocells but Does Not Select for Them Relative to Unjoined Amino Acids. The Journal of Physical Chemistry B. 125(29). 7933–7939. 12 indexed citations
9.
Lee, Kimberly A., Paul S. Andrews, Matthew P. Stokes, et al.. (2011). Ubiquitin Ligase Substrate Identification through Quantitative Proteomics at Both the Protein and Peptide Levels. Journal of Biological Chemistry. 286(48). 41530–41538. 70 indexed citations
10.
Chen, Peter, John K. McGuire, Robert C. Hackman, et al.. (2008). Tissue Inhibitor of Metalloproteinase-1 Moderates Airway Re-Epithelialization by Regulating Matrilysin Activity. American Journal Of Pathology. 172(5). 1256–1270. 48 indexed citations
11.
Li, Nianyu, Yao Wang, Karen Forbes, et al.. (2007). Metalloproteases regulate T‐cell proliferation and effector function via LAG‐3. The EMBO Journal. 26(2). 494–504. 212 indexed citations
12.
Loesch, Kimberly, Luqin Deng, Xiangdong Wang, et al.. (2006). Janus Kinase 2 Influences Growth Hormone Receptor Metalloproteolysis. Endocrinology. 147(6). 2839–2849. 30 indexed citations
13.
Zhang, Yue, Ran Guan, Jing Jiang, et al.. (2001). Growth Hormone (GH)-induced Dimerization Inhibits Phorbol Ester-stimulated GH Receptor Proteolysis. Journal of Biological Chemistry. 276(27). 24565–24573. 82 indexed citations
14.
Guan, Ran, Yue Zhang, Jing Jiang, et al.. (2001). Phorbol Ester- and Growth Factor-Induced Growth Hormone (GH) Receptor Proteolysis and GH-Binding Protein Shedding: Relationship to GH Receptor Down-Regulation1. Endocrinology. 142(3). 1137–1147. 44 indexed citations
15.
Frank, Stuart J., et al.. (2000). Insights into modulation of (and by) growth hormone signaling. Journal of Laboratory and Clinical Medicine. 136(1). 14–20. 5 indexed citations
16.
Sadhukhan, Ramkrishna, et al.. (1999). Unaltered Cleavage and Secretion of Angiotensin-converting Enzyme in Tumor Necrosis Factor-α-converting Enzyme-deficient Mice. Journal of Biological Chemistry. 274(15). 10511–10516. 46 indexed citations
17.
Peschon, Jacques J., Jennifer L. Slack, P. Linga Reddy, et al.. (1998). An Essential Role for Ectodomain Shedding in Mammalian Development. Science. 282(5392). 1281–1284. 1355 indexed citations breakdown →
18.
Black, Roy A., Fiona H. Durie, Robert D. Miller, et al.. (1996). Relaxed Specificity of Matrix Metalloproteinases (MMPS) and TIMP Insensitivity of Tumor Necrosis Factor-α (TNF-α) Production Suggest the Major TNF-α Converting Enzyme Is Not an MMP. Biochemical and Biophysical Research Communications. 225(2). 400–405. 60 indexed citations
19.
Crowe, Paul D., et al.. (1995). A metalloprotease inhibitor blocks shedding of the 80-kD TNF receptor and TNF processing in T lymphocytes.. The Journal of Experimental Medicine. 181(3). 1205–1210. 213 indexed citations
20.
Black, Roy A., С. Kronheim, & Paul R. Sleath. (1989). Activation of interleukin‐ 1β by a co‐induced protease. FEBS Letters. 247(2). 386–390. 211 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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