Bin Nan

6.3k citations
119 papers · 4.4k indexed · 1 hit paper · h-index 41

Bin Nan

116 papers receiving 4.3k citations

Hit Papers

Trends in glucagon-like peptide 1 receptor agonist use, 2...862023202620242025255075

Peers

Bin Nan
Comparison fields: 5 of 180
  • Ophthalmology 606
  • Statistics and Probability 539
  • Reproductive Medicine 292
  • Radiology, Nuclear Medicine and Imaging 741
  • Endocrinology, Diabetes and Metabolism 476
Replace Satoshi Teramukai with:
Satoshi Teramukai Japan
Matthieu Resche‐Rigon France
Takuhiro Yamaguchi Japan
Tim Sprosen United Kingdom
Harald Heinzl Austria
Toshimitsu Hamasaki Japan
Thomas R. Fears United States
Nuala A. Sheehan United Kingdom
Paul Downey Ireland
Theodore Colton United States
Bin Nan relative to Satoshi Teramukai Japan Satoshi Teramukai's profile →
Citations per field
00.5×3.4×
Satoshi Teramukai · 1×
Citations per year

Countries citing papers authored by Bin Nan

Since Specialization
Citations

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

Fields of papers citing papers by Bin Nan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20240
2 202310
3
Trends in glucagon-like peptide 1 receptor agonist use, 2014 to 2022breakdown →
202386
4 20221
5
Optimizing Electronic Release of Imaging Results through an Online Patient Portal
20191
6 20199
7 20197
8 201815
9
Targeting aging: Geroprotective Drug Metformin Reduces Risk of Adult-onset Open-angle Glaucoma
20141
10 201310
11 201336
12 201235
13 201133
14 2011147
15 20111
16 2011113
17 201071
18 200540
19 20051
20 200133

About Bin Nan

Bin Nan is a scholar working on Statistics and Probability, Ophthalmology, Radiology, Nuclear Medicine and Imaging, Endocrinology, Diabetes and Metabolism and Reproductive Medicine, having authored 119 papers that have together received 4.4k indexed citations. Recurring topics across this work include Statistical Methods and Inference (28 papers), Statistical Methods and Bayesian Inference (18 papers), Advanced Causal Inference Techniques (12 papers), Retinal Diseases and Treatments (11 papers), Glaucoma and retinal disorders (10 papers), Hormonal and reproductive studies (8 papers), Ovarian function and disorders (6 papers) and Menopause: Health Impacts and Treatments (6 papers). The work is most often cited by research in Ophthalmology (606 citations), Statistics and Probability (539 citations), Reproductive Medicine (292 citations), Radiology, Nuclear Medicine and Imaging (741 citations) and Endocrinology, Diabetes and Metabolism (476 citations). Bin Nan has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Joshua D. Stein, Sioḃán D. Harlow, Nidhi Talwar, John F. Randolph, Ji Zhu, Huiyong Zheng, Daniel McConnell, David C. Musch, Norman E. Breslow and Daniel M. Green. Their work appears in journals such as The Journal of Clinical Endocrinology & Metabolism, Ophthalmology, Biometrics, American Journal of Roentgenology and Journal of the American Statistical Association.

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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