Pei-Ling Chu

6.3k citations
11 papers · 186 · h-index 9

Impact in

Papers in

Pei-Ling Chu

11 papers receiving 178 citations

Peers

Pei-Ling Chu
Comparison fields: 5 of 54
  • Endocrinology, Diabetes and Metabolism 71
  • Statistics and Probability 27
  • Health, Toxicology and Mutagenesis 30
  • Hepatology 14
  • Management Science and Operations Research 19
Replace Mei Kuen Tang with:
Mei Kuen Tang United States
Kazuhide Ishikura Japan
Evangelia E. Tsakiridis Canada
Raiya Sarwar United States
Yoshiki Tsuboi Japan
Ioanna Panagiota Kalafati Greece
Peter R. Gilbert United States
Barbara Fruci Italy
Anchen Shi China
Pei-Ling Chu relative to Mei Kuen Tang United States Mei Kuen Tang's profile →
Citations per field
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Citations per year

Countries citing papers authored by Pei-Ling Chu

Since Specialization
Citations

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

Fields of papers citing papers by Pei-Ling Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pei-Ling Chu. 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 Pei-Ling Chu. The network helps show where Pei-Ling Chu may publish in the future.

Co-authors

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

All Works

11 of 11 papers shown
#Work
1 200936
2 200827
3 201825
4 200925
5 201621
6 201516
7 200514
8 20119
9 20088
10 20124
11 20251

About Pei-Ling Chu

Pei-Ling Chu is a scholar working on Endocrinology, Diabetes and Metabolism, Molecular Biology, Health, Toxicology and Mutagenesis, Pollution and Pulmonary and Respiratory Medicine, having authored 11 papers that have together received 186 indexed citations. Recurring topics across this work include Diabetes Management and Research (3 papers), Pesticide and Herbicide Environmental Studies (1 paper), Gene expression and cancer classification (1 paper), Statistical Methods in Clinical Trials (1 paper), Diabetes Treatment and Management (1 paper), Metabolism, Diabetes, and Cancer (1 paper), Pesticide Exposure and Toxicity (1 paper) and Lung Cancer Treatments and Mutations (1 paper). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (71 citations), Statistics and Probability (27 citations), Health, Toxicology and Mutagenesis (30 citations), Hepatology (14 citations) and Management Science and Operations Research (19 citations). Pei-Ling Chu has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Yong Lin, Weichung Joe Shih, Bruce S. Trippe, Anuj Bhargava, David S. Oyer, Mark D. Shepherd, Mohammed Abdul Majeed, Jason Brett, Poul Jennum and Jinchun Liu. Their work appears in journals such as Journal of Clinical Oncology, Diabetes Care, The Science of The Total Environment, Canadian Journal of Diabetes and Atmospheric Environment.

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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