Biyi Shen

417 citations
31 papers · 217 · h-index 10

Impact in

    • Statistical Methods and Inference
    • Statistical Methods and Bayesian Inference
    • Advanced Causal Inference Techniques
    • Peripheral Artery Disease Management

Papers in

    • Peripheral Artery Disease Management 10
    • Statistical Methods and Inference 6
    • Statistical Methods and Bayesian Inference 4
    • Advanced Causal Inference Techniques 3

Biyi Shen

27 papers receiving 216 citations

Peers

Biyi Shen
Comparison fields: 5 of 61
  • Statistics and Probability 21
  • Surgery 57
  • Endocrinology, Diabetes and Metabolism 15
  • Endocrine and Autonomic Systems 6
  • Health Informatics 1
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Countries citing papers authored by Biyi Shen

Since Specialization
Citations

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

Fields of papers citing papers by Biyi Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201928
2 202123
3 202220
4 201918
5 202015
6 202113
7 202212
8 202210
9 202110
10 202010
11 20209
12 20208
13 20208
14 20225
15 20214
16 20244
17 20224
18 20213
19 20223
20 20232

About Biyi Shen

Biyi Shen is a scholar working on Surgery, Statistics and Probability, Pathology and Forensic Medicine, Molecular Biology and Oncology, having authored 31 papers that have together received 217 indexed citations. Recurring topics across this work include Peripheral Artery Disease Management (10 papers), Statistical Methods and Inference (6 papers), Lymphoma Diagnosis and Treatment (5 papers), Statistical Methods and Bayesian Inference (4 papers), Advanced Causal Inference Techniques (3 papers), Chronic Lymphocytic Leukemia Research (3 papers), Lymphatic System and Diseases (2 papers) and CAR-T cell therapy research (2 papers). The work is most often cited by research in Statistics and Probability (21 citations), Surgery (57 citations), Endocrinology, Diabetes and Metabolism (15 citations), Endocrine and Autonomic Systems (6 citations) and Health Informatics (1 citation). Biyi Shen has collaborated with scholars based in United States, Spain and China. Frequent co-authors include Ming Wang, Polly S. Montgomery, Andrew W. Gardner, Chixiang Chen, Nicholas G. Zaorsky, Leila T. Tchelebi, Ana I. Casanegra, Vonn Walter, Scott A. Berceli and Rongling Wu. Their work appears in journals such as Journal of Vascular Surgery, Journal of Cardiopulmonary Rehabilitation and Prevention, Statistics in Medicine, Blood and Biometrical Journal.

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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