John S. Crabb

1.6k total citations
17 papers, 831 citations indexed

About

John S. Crabb is a scholar working on Ophthalmology, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, John S. Crabb has authored 17 papers receiving a total of 831 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Ophthalmology, 11 papers in Molecular Biology and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in John S. Crabb's work include Retinal Diseases and Treatments (12 papers), Retinal Development and Disorders (9 papers) and Glaucoma and retinal disorders (8 papers). John S. Crabb is often cited by papers focused on Retinal Diseases and Treatments (12 papers), Retinal Development and Disorders (9 papers) and Glaucoma and retinal disorders (8 papers). John S. Crabb collaborates with scholars based in United States, India and United Kingdom. John S. Crabb's co-authors include John W. Crabb, Vera L. Bonilha, Xiaorong Gu, Joe G. Hollyfield, Sanjoy K. Bhattacharya, Mary E. Rayborn, K.G. Shadrach, Xianglin Yuan, Xianglin Yuan and Xiuzhen Yue and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and Investigative Ophthalmology & Visual Science.

In The Last Decade

John S. Crabb

15 papers receiving 818 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John S. Crabb United States 14 545 492 196 107 72 17 831
Shandiz Tehrani United States 14 549 1.0× 338 0.7× 392 2.0× 197 1.8× 65 0.9× 29 1.0k
H. Thomas Steely United States 13 594 1.1× 512 1.0× 171 0.9× 206 1.9× 27 0.4× 18 899
Sachiko Kuroiwa Japan 15 456 0.8× 397 0.8× 276 1.4× 38 0.4× 44 0.6× 16 778
Sowmya Parameswaran India 14 171 0.3× 451 0.9× 116 0.6× 34 0.3× 41 0.6× 40 637
Hoseong S. Yang United States 6 559 1.0× 482 1.0× 285 1.5× 44 0.4× 47 0.7× 6 812
Manlin Jin United States 11 306 0.6× 373 0.8× 209 1.1× 55 0.5× 39 0.5× 12 615
E. Antecka Canada 18 351 0.6× 319 0.6× 112 0.6× 66 0.6× 244 3.4× 59 843
H.J. Winkens Netherlands 18 607 1.1× 697 1.4× 73 0.4× 115 1.1× 164 2.3× 35 1.1k
Peter A. Campochiaro United States 7 534 1.0× 411 0.8× 283 1.4× 46 0.4× 27 0.4× 10 709
Arcilee Frost United States 7 493 0.9× 369 0.8× 342 1.7× 37 0.3× 78 1.1× 9 726

Countries citing papers authored by John S. Crabb

Since Specialization
Citations

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

Fields of papers citing papers by John S. Crabb

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John S. Crabb. 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 John S. Crabb. The network helps show where John S. Crabb may publish in the future.

Co-authorship network of co-authors of John S. Crabb

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

All Works

17 of 17 papers shown
1.
Singh, Charandeep, Sonal Dalvi, Geeng-Fu Jang, et al.. (2024). Tissue Inhibitor of Metalloproteinase 3 (TIMP3) mutations increase glycolytic activity and dysregulate glutamine metabolism in RPE cells. Molecular Metabolism. 88. 101995–101995. 1 indexed citations
2.
Jang, Geeng-Fu, John S. Crabb, Shi‐Ming Luo, et al.. (2024). Quantitative proteomic profiling reveals sexual dimorphism in the retina and RPE of C57BL6 mice. Biology of Sex Differences. 15(1). 87–87.
3.
Crabb, John W., John S. Crabb, Bo Hu, et al.. (2015). iTRAQ Quantitative Proteomic Comparison of Metastatic and Non-Metastatic Uveal Melanoma Tumors. PLoS ONE. 10(8). e0135543–e0135543. 26 indexed citations
4.
Gu, Xiaorong, Zhenbo Hu, Quteba Ebrahem, et al.. (2014). Runx1 Regulation of Pu.1 Corepressor/Coactivator Exchange Identifies Specific Molecular Targets for Leukemia Differentiation Therapy. Journal of Biological Chemistry. 289(21). 14881–14895. 26 indexed citations
5.
Renganathan, Kutralanathan, Mary E. Rayborn, John S. Crabb, et al.. (2013). CEP Biomarkers as Potential Tools for Monitoring Therapeutics. PLoS ONE. 8(10). e76325–e76325. 17 indexed citations
6.
Gu, Xiaorong, John S. Crabb, John W. Crabb, et al.. (2012). Age-Related Changes in the Retinal Pigment Epithelium (RPE). PLoS ONE. 7(6). e38673–e38673. 65 indexed citations
7.
Bollinger, Kathryn, et al.. (2012). Proteomic similarities in steroid responsiveness in normal and glaucomatous trabecular meshwork cells.. PubMed. 18. 2001–11. 31 indexed citations
8.
Crabb, John W., et al.. (2011). Quantitative Proteomic Studies Implicate Mitochondrial Dysfunction in the Trabecular Meshwork in Glaucoma Pathology. Investigative Ophthalmology & Visual Science. 52(14). 6611–6611. 1 indexed citations
9.
Bollinger, Kathryn, et al.. (2011). Quantitative Proteomics: TGFβ2Signaling in Trabecular Meshwork Cells. Investigative Ophthalmology & Visual Science. 52(11). 8287–8287. 57 indexed citations
10.
Yuan, Xianglin, Xiaorong Gu, John S. Crabb, et al.. (2010). Quantitative Proteomics: Comparison of the Macular Bruch Membrane/Choroid Complex from Age-related Macular Degeneration and Normal Eyes. Molecular & Cellular Proteomics. 9(6). 1031–1046. 121 indexed citations
11.
Crabb, John W., Xianglin Yuan, Galina Dvoriantchikova, et al.. (2010). Preliminary quantitative proteomic characterization of glaucomatous rat retinal ganglion cells. Experimental Eye Research. 91(1). 107–110. 24 indexed citations
12.
Gugiu, Bogdan G., Kutralanathan Renganathan, Xiaorong Gu, et al.. (2008). Retinal Pigment Epithelium Lipofuscin Proteomics. Molecular & Cellular Proteomics. 7(7). 1397–1405. 132 indexed citations
13.
Bonilha, Vera L., Mary E. Rayborn, Sanjoy K. Bhattacharya, et al.. (2007). The Retinal Pigment Epithelium Apical Microvilli and Retinal Function. Advances in experimental medicine and biology. 572. 519–524. 40 indexed citations
14.
Bhattacharya, Sanjoy K., John S. Crabb, Vera L. Bonilha, et al.. (2006). Proteomics Implicates Peptidyl Arginine Deiminase 2 and Optic Nerve Citrullination in Glaucoma Pathogenesis. Investigative Ophthalmology & Visual Science. 47(6). 2508–2508. 88 indexed citations
15.
Bhattacharya, Sanjoy K., Edward J. Rockwood, Scott D. Smith, et al.. (2004). Proteomics Reveal Cochlin Deposits Associated with Glaucomatous Trabecular Meshwork. Journal of Biological Chemistry. 280(7). 6080–6084. 116 indexed citations
16.
West, Karen A., Lin Yan, K.G. Shadrach, et al.. (2003). Protein Database, Human Retinal Pigment Epithelium. Molecular & Cellular Proteomics. 2(1). 37–49. 49 indexed citations
17.
West, Karen A., Lin Yan, Masaru Miyagi, et al.. (2001). Proteome Survey of Proliferating and Differentiating Rat RPE-J Cells. Experimental Eye Research. 73(4). 479–491. 37 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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