Ying‐Ying Lee

595 citations
28 papers · 319 · h-index 12

Impact in

Papers in

    • Glycosylation and Glycoproteins Research 3
    • Protein Structure and Dynamics 3
    • Statistical Methods and Bayesian Inference 7
    • Statistical Methods and Inference 7
    • Advanced Causal Inference Techniques 4
    • Advanced Statistical Methods and Models 2

Ying‐Ying Lee

25 papers receiving 316 citations

Peers

Ying‐Ying Lee
Comparison fields: 5 of 77
  • Statistics and Probability 37
  • Hepatology 28
  • Biochemistry 23
  • Cancer Research 40
  • Molecular Biology 170
Replace Tammy M. Havener with:
Tammy M. Havener United States
J. Shawn Jones United States
Agneta Freijs Sweden
Mario Schindeldecker Germany
Nina C. Gundacker Austria
C. Widakowich Belgium
Junchi Hu China
Fernando Salgado-Polo Netherlands
Anuradha Nalli United States
Ying‐Ying Lee relative to Tammy M. Havener United States Tammy M. Havener's profile →
Citations per field
00.5×9.3×
Tammy M. Havener · 1×
Citations per year

Countries citing papers authored by Ying‐Ying Lee

Since Specialization
Citations

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

Fields of papers citing papers by Ying‐Ying Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ying‐Ying Lee. 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 Ying‐Ying Lee. The network helps show where Ying‐Ying Lee may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201455
2 201447
3 201439
4 201836
5 201218
6 201515
7 201415
8 202014
9 201212
10 201812
11 201311
12 202111
13 20176
14 20165
15 20174
16 20194
17 20183
18 20242
19 20182
20 20102

About Ying‐Ying Lee

Ying‐Ying Lee is a scholar working on Molecular Biology, Statistics and Probability, Economics and Econometrics, Materials Chemistry and Physiology, having authored 28 papers that have together received 319 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (7 papers), Statistical Methods and Inference (7 papers), Advanced Causal Inference Techniques (4 papers), Enzyme Structure and Function (4 papers), Glycosylation and Glycoproteins Research (3 papers), Protein Structure and Dynamics (3 papers), Advanced Statistical Methods and Models (2 papers) and Economic and Environmental Valuation (2 papers). The work is most often cited by research in Statistics and Probability (37 citations), Hepatology (28 citations), Biochemistry (23 citations), Cancer Research (40 citations) and Molecular Biology (170 citations). Ying‐Ying Lee has collaborated with scholars based in United States, Hong Kong and Taiwan. Frequent co-authors include Lesa J. Beamer, Kyle M. Stiers, Bailee Kain, Alfred S.L. Cheng, Yue S. Cheung, Cristina M. Furdui, Zhuo Yu, Paul B.S. Lai, Joseph J.�Y. Sung and Hai Feng. Their work appears in journals such as Nucleic Acids Research, Journal of Econometrics, Journal of Biological Chemistry, Gastroenterology and The Review of Economics and Statistics.

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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