Zhezhen Jin

10.6k citations
256 papers · 7.1k indexed · h-index 45

Zhezhen Jin

245 papers receiving 6.9k citations

Peers

Zhezhen Jin
Comparison fields: 5 of 171
  • Cardiology and Cardiovascular Medicine 2.9k
  • Transplantation 266
  • Statistics and Probability 706
  • Hematology 505
  • Radiology, Nuclear Medicine and Imaging 1.0k
Replace Nancy L. Geller with:
Nancy L. Geller United States
Martin Landray United Kingdom
Joseph Beyene Canada
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Claudia Schmoor Germany
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Zhezhen Jin relative to Nancy L. Geller United States Nancy L. Geller's profile →
Citations per field
00.5×1.5×2.4×
Nancy L. Geller · 1×
Citations per year

Countries citing papers authored by Zhezhen Jin

Since Specialization
Citations

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

Fields of papers citing papers by Zhezhen Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Zhezhen Jin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Zhezhen Jin Line = papers co-authored together Zhezhen Jin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20255
3 20240
4 20240
5 20241
6 20230
7 202211
8 20224
9 20215
10 202053
11 201621
12 201644
13 201539
14 20147
15
Abstract 15940: Prognostic Value of Left Ventricular Global Longitudinal Strain in the Elderly: A Population-Based Prospective Cohort Study
20131
16 20114
17 201081
18 20096
19
Design-adaptive minimax local linear regression for longitudinal/clustered data
20086
20 200732

About Zhezhen Jin

Zhezhen Jin is a scholar working on Hematology, Cardiology and Cardiovascular Medicine and Transplantation, having authored 256 papers that have together received 7.1k indexed citations. Recurring topics across this work include Cardiovascular Function and Risk Factors (42 papers), Hematopoietic Stem Cell Transplantation (38 papers), Cardiac Imaging and Diagnostics (28 papers), Acute Lymphoblastic Leukemia research (24 papers), Cardiovascular Health and Disease Prevention (22 papers), Childhood Cancer Survivors' Quality of Life (22 papers), Statistical Methods and Inference (19 papers) and Hemoglobinopathies and Related Disorders (17 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (2.9k citations), Transplantation (266 citations) and Statistics and Probability (706 citations). Zhezhen Jin has collaborated with scholars based in United States, Japan and United Kingdom. Frequent co-authors include Marco R. Di Tullio, Tatjana Rundek, Ralph L. Sacco, Mitchell S.V. Elkind, Shunichi Homma, C Allison Russo, Shunichi Homma, Ralph L. Sacco, Bernadette Boden‐Albala and Prakash Satwani. Their work appears in journals such as Circulation, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

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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