Jun Wang

257 papers receiving 6.3k citations

Jun Wang's Hit Papers

Meta-analysis of modifiable risk factors for Alzheimer's disease 2015 · 431 citations
4310+3+7Years since publication100200300400

Peers

Jun Wang
Comparison fields: 5 of 164
  • Cancer Research 911
  • Pathology and Forensic Medicine 713
  • Geriatrics and Gerontology 131
  • Oncology 932
  • Pharmacology 519
Replace Javier Dı́ez with:
Javier Dı́ez Spain
Atsushi Takahashi Japan
Tsutomu Imaizumi Japan
Leong L. Ng United Kingdom
Masataka Sata Japan
Rosario Scalia United States
Fabrizio Montecucco Italy
Tohru Minamino Japan
Koichi Node Japan
Yves Cottin France
Jun Wang relative to Javier Dı́ez Spain Javier Dı́ez's profile →
Citations per field
00.5×1.7×
Javier Dı́ez · 1×
Citations per year

Countries citing papers authored by Jun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Jun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Wang, 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 Jun Wang Line = papers co-authored together Jun Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 277 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Meta-analysis of modifiable risk factors for Alzheimer's disease
Hit paper breakdown →
2015431
2 2013246
3 1998196
4 2008139
5 2002135
6 2012129
7 2004125
8 2007119
9 2014107
10
Defects of DNA mismatch repair in human prostate cancer.
2001103
11
Metabolic consequences of a reversed pH gradient in rat tumors.
1994103
12
Placenta inflammation is closely associated with gestational diabetes mellitus.
2021102
13 2016102
14 200399
15 201193
16 201892
17 200392
18 200686
19 199984
20 201581

About Jun Wang

Jun Wang is a scholar working on Molecular Biology, Oncology, Cancer Research, Pathology and Forensic Medicine and Pulmonary and Respiratory Medicine, having authored 277 papers that have together received 6.5k indexed citations. Recurring topics across this work include Cancer, Lipids, and Metabolism (17 papers), Estrogen and related hormone effects (13 papers), Epigenetics and DNA Methylation (13 papers), Cannabis and Cannabinoid Research (12 papers), Breast Cancer Treatment Studies (11 papers), Pregnancy and preeclampsia studies (10 papers), Protease and Inhibitor Mechanisms (8 papers) and Blood Coagulation and Thrombosis Mechanisms (8 papers). The work is most often cited by research in Cancer Research (911 citations), Pathology and Forensic Medicine (713 citations), Geriatrics and Gerontology (131 citations), Oncology (932 citations) and Pharmacology (519 citations). Jun Wang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Natsuo Ueda, Hau C. Kwaan, Coral A. Lamartiniere, Meng‐Shan Tan, Lan Tan, Teng Jiang, Lin Tan, Hui-Fu Wang, Qing‐Fei Zhao and Jieqiong Li. Their work appears in journals such as Breast Cancer Research and Treatment, Frontiers in Oncology, Journal of Cancer, Cancer Prevention Research and PLoS ONE.

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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