Paul Yan

823 total citations
9 papers, 380 citations indexed

About

Paul Yan is a scholar working on Nephrology, Molecular Biology and Genetics. According to data from OpenAlex, Paul Yan has authored 9 papers receiving a total of 380 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Nephrology, 3 papers in Molecular Biology and 2 papers in Genetics. Recurrent topics in Paul Yan's work include Renal Diseases and Glomerulopathies (4 papers), Chronic Kidney Disease and Diabetes (3 papers) and Chronic Lymphocytic Leukemia Research (2 papers). Paul Yan is often cited by papers focused on Renal Diseases and Glomerulopathies (4 papers), Chronic Kidney Disease and Diabetes (3 papers) and Chronic Lymphocytic Leukemia Research (2 papers). Paul Yan collaborates with scholars based in United States, China and Canada. Paul Yan's co-authors include Martin R. Pollak, Johannes Schlöndorff, Seth L. Alper, David J. Friedman, Lynn VerPlank, Jung Hee Suh, Nathan H. Zahler, Opeyemi A. Olabisi, John F. Heneghan and Salvatore DiBartolo and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Kidney International and Journal of the American Society of Nephrology.

In The Last Decade

Paul Yan

9 papers receiving 378 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Paul Yan United States	8	230	169	90	39	31	9	380
Alison Homstad United States	6	287 1.2×	187 1.1×	82 0.9×	64 1.6×		8	406
Juan Tao China	13	57 0.2×	83 0.5×	15 0.2×	23 0.6×	8 0.3×	34	368
Marie-Helena Docherty United Kingdom	3	92 0.4×	150 0.9×	22 0.2×	19 0.5×	8 0.3×	4	358
Saori Miwa Japan	9	102 0.4×	265 1.6×	18 0.2×	44 1.1×	6 0.2×	12	462
Rafael Muley Spain	9	122 0.5×	166 1.0×	24 0.3×	24 0.6×	2 0.1×	11	336
Shunji Shiohira Japan	8	219 1.0×	125 0.7×	20 0.2×	88 2.3×	3 0.1×	19	361
Mayuko Ohno Japan	11	160 0.7×	532 3.1×	9 0.1×	54 1.4×	3 0.1×	11	679
Steve J Harper United Kingdom	8	123 0.5×	252 1.5×	13 0.1×	18 0.5×	14 0.5×	10	428
Elena Passeri Italy	12	30 0.1×	129 0.8×	15 0.2×	36 0.9×	3 0.1×	23	329
R Paschke Germany	13	26 0.1×	214 1.3×	17 0.2×	72 1.8×	12 0.4×	25	596

Countries citing papers authored by Paul Yan

Since Specialization

Citations

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

Fields of papers citing papers by Paul Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Yan

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

Saif, Nabeel, Paul Yan, Olivia Scheyer, et al.. (2020). Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer's Disease Dementia. The Journal of Prevention of Alzheimer s Disease. 7(2). 104–111. 24 indexed citations

Subramanian, Balajikarthick, Justin Chun, Paul Yan, et al.. (2020). FSGS-Causing INF2 Mutation Impairs Cleaved INF2 N-Fragment Functions in Podocytes. Journal of the American Society of Nephrology. 31(2). 374–391. 17 indexed citations

Pai, Athma A., Joseph M. Paggi, Paul Yan, Karen Adelman, & Christopher B. Burge. (2018). Numerous recursive sites contribute to accuracy of splicing in long introns in flies. PLoS Genetics. 14(8). e1007588–e1007588. 12 indexed citations

Wang, Minxian, Lei Tian, Giulio Genovese, et al.. (2018). UBD modifies APOL1 -induced kidney disease risk. Proceedings of the National Academy of Sciences. 115(13). 3446–3451. 45 indexed citations

Sun, Zhongjie, et al.. (2017). [Gene expression profiles of long non-coding RNAs in human degenerated intervertebral disc tissue].. PubMed. 97(33). 2582–2586. 1 indexed citations

Subramanian, Balajikarthick, Hua Sun, Paul Yan, et al.. (2016). Mice with mutant Inf2 show impaired podocyte and slit diaphragm integrity in response to protamine-induced kidney injury. Kidney International. 90(2). 363–372. 35 indexed citations

Schepelmann, Martin, Sarah C. Brennan, Wenhan Chang, et al.. (2015). Comparative expression of the extracellular calcium-sensing receptor in the mouse, rat, and human kidney. American Journal of Physiology-Renal Physiology. 310(6). F518–F533. 45 indexed citations

Olabisi, Opeyemi A., Lynn VerPlank, Nathan H. Zahler, et al.. (2015). APOL1 kidney disease risk variants cause cytotoxicity by depleting cellular potassium and inducing stress-activated protein kinases. Proceedings of the National Academy of Sciences. 113(4). 830–837. 153 indexed citations

Molday, Laurie L., Paul Yan, Sanford L. Boye, et al.. (2013). RD3 gene delivery restores guanylate cyclase localization and rescues photoreceptors in the Rd3 mouse model of Leber congenital amaurosis 12. Human Molecular Genetics. 22(19). 3894–3905. 48 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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