Zhan Ye

1.8k total citations
58 papers, 782 citations indexed

About

Zhan Ye is a scholar working on Genetics, Molecular Biology and Surgery. According to data from OpenAlex, Zhan Ye has authored 58 papers receiving a total of 782 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Genetics, 22 papers in Molecular Biology and 6 papers in Surgery. Recurrent topics in Zhan Ye's work include Genetic Associations and Epidemiology (11 papers), Genomics and Rare Diseases (8 papers) and Biomedical Text Mining and Ontologies (6 papers). Zhan Ye is often cited by papers focused on Genetic Associations and Epidemiology (11 papers), Genomics and Rare Diseases (8 papers) and Biomedical Text Mining and Ontologies (6 papers). Zhan Ye collaborates with scholars based in United States, China and Italy. Zhan Ye's co-authors include John Mayer, Simon Lin, Scott J. Hebbring, Steven J. Schrodi, Susan C. Straley, Majid Rastegar-Mojarad, Murray H. Brilliant, Clarissa Cowan, Alexander Philipovskiy and Murray H. Brilliant and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Zhan Ye

52 papers receiving 769 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhan Ye United States 17 276 268 76 76 58 58 782
Segun Fatumo United Kingdom 17 349 1.3× 386 1.4× 31 0.4× 64 0.8× 31 0.5× 90 1.0k
Neil F. Abernethy United States 12 89 0.3× 168 0.6× 102 1.3× 84 1.1× 26 0.4× 35 735
Diarmuid O’Donoghue Ireland 16 218 0.8× 277 1.0× 97 1.3× 90 1.2× 199 3.4× 49 1.1k
Sidhartha R. Sinha United States 17 348 1.3× 550 2.1× 34 0.4× 138 1.8× 222 3.8× 41 1.3k
Alice Matimba South Africa 15 210 0.8× 209 0.8× 14 0.2× 51 0.7× 27 0.5× 37 784
Tong Tong Wu United States 20 350 1.3× 416 1.6× 137 1.8× 40 0.5× 50 0.9× 88 1.7k
Min Seob Kwak South Korea 17 206 0.7× 103 0.4× 79 1.0× 108 1.4× 337 5.8× 82 1.1k
Ghassan Ghandour United States 17 285 1.0× 812 3.0× 86 1.1× 261 3.4× 93 1.6× 29 1.8k
Binyou Wang China 17 72 0.3× 257 1.0× 12 0.2× 53 0.7× 64 1.1× 75 964
Sally N. Adebamowo United States 19 169 0.6× 291 1.1× 16 0.2× 37 0.5× 130 2.2× 65 1.1k

Countries citing papers authored by Zhan Ye

Since Specialization
Citations

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

Fields of papers citing papers by Zhan Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhan Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Zhan Ye. A scholar is included among the top collaborators of Zhan Ye 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 Zhan Ye. Zhan Ye 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.
2.
Li, Xi, Elena Peeva, Yuji Yamaguchi, et al.. (2025). Multiomics Analysis of the Response to Ritlecitinib in Alopecia Areata Subtypes and Correlation With Efficacy. Allergy. 80(8). 2348–2360.
3.
Li, Xi, Elena Peeva, Yuji Yamaguchi, et al.. (2025). Integrated Meta-Analysis of Scalp Transcriptomics and Serum Proteomics Defines Alopecia Areata Subtypes and Core Disease Pathways. International Journal of Molecular Sciences. 26(19). 9662–9662.
4.
Peeva, Elena, Yuji Yamaguchi, Zhan Ye, et al.. (2024). Efficacy and safety of ritlecitinib in vitiligo patients across Fitzpatrick skin types with biomarker analyses. Experimental Dermatology. 33(9). e15177–e15177. 2 indexed citations
5.
Yamaguchi, Yuji, Elena Peeva, Ester Del Duca, et al.. (2024). Ritlecitinib, a JAK3/TEC family kinase inhibitor, stabilizes active lesions and repigments stable lesions in vitiligo. Archives of Dermatological Research. 316(7). 478–478. 7 indexed citations
6.
Ye, Zhan, John Mayer, Terrie Kitchner, et al.. (2023). Estimating the efficacy of pharmacogenomics over a lifetime. Frontiers in Medicine. 10. 1006743–1006743. 4 indexed citations
7.
Pulia, Michael S., Jennifer L. Anderson, Zhan Ye, et al.. (2022). Expression of Staphylococcal Virulence Genes In Situ in Human Skin and Soft Tissue Infections. Antibiotics. 11(4). 527–527. 6 indexed citations
8.
Ye, Zhan, et al.. (2019). Carrier frequency estimation of Zellweger spectrum disorder using ExAC database and bioinformatics tools. Genetics in Medicine. 21(9). 1969–1976. 11 indexed citations
9.
Karnes, Jason H., Lisa Bastarache, Christian M. Shaffer, et al.. (2017). Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Science Translational Medicine. 9(389). 74 indexed citations
10.
Elston, Robert C., Guilherme J. M. Rosa, John Mayer, et al.. (2017). Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics. 34(4). 635–642. 6 indexed citations
11.
12.
Mosley, Jonathan D., John S. Witte, Emma K. Larkin, et al.. (2016). Identifying genetically driven clinical phenotypes using linear mixed models. Nature Communications. 7(1). 11433–11433. 8 indexed citations
13.
Rastegar-Mojarad, Majid, et al.. (2015). Collecting and Analyzing Patient Experiences of Health Care From Social Media. SHILAP Revista de lepidopterología. 4(3). e78–e78. 45 indexed citations
14.
Ye, Zhan, et al.. (2014). A Review of Clinical Decision Support Products in Dentistry.. AMIA.
15.
Schrodi, Steven J., Shubhabrata Mukherjee, Ying Shan, et al.. (2014). Genetic-based prediction of disease traits: prediction is very difficult, especially about the future†. Frontiers in Genetics. 5. 162–162. 48 indexed citations
16.
Ji, Xiaonan, Tara Borlawsky, Zhan Ye, et al.. (2014). Enabling Online Studies of Conceptual Relationships Between Medical Terms: Developing an Efficient Web Platform. JMIR Medical Informatics. 2(2). e23–e23. 8 indexed citations
17.
Ye, Zhan, John Mayer, Lynn Ivacic, et al.. (2014). Phenome-wide association studies (PheWASs) for functional variants. European Journal of Human Genetics. 23(4). 523–529. 35 indexed citations
18.
Ye, Zhan, Daniel A. Vasco, Tonia C. Carter, et al.. (2014). Genome wide association study of SNP-, gene-, and pathway-based approaches to identify genes influencing susceptibility to Staphylococcus aureus infections. Frontiers in Genetics. 5. 125–125. 33 indexed citations
19.
Uittenbogaard, Annette, R. Lakshman Chelvarajan, Tanya Myers‐Morales, et al.. (2012). Toward a molecular pathogenic pathway for Yersinia pestis YopM. Frontiers in Cellular and Infection Microbiology. 2. 155–155. 11 indexed citations
20.
Ye, Zhan, Elizabeth J. Atkinson, Brooke L. Fridley, & Mariza de Andrade. (2007). Comparison of variable and model selection methods for genetic association studies using the GAW15 simulated data. BMC Proceedings. 1(S1). S34–S34. 1 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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