Dee Pei

4.6k total citations
25 papers, 347 citations indexed

About

Dee Pei is a scholar working on Nephrology, Epidemiology and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Dee Pei has authored 25 papers receiving a total of 347 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Nephrology, 9 papers in Epidemiology and 9 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Dee Pei's work include Alcohol Consumption and Health Effects (7 papers), Liver Disease Diagnosis and Treatment (6 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (6 papers). Dee Pei is often cited by papers focused on Alcohol Consumption and Health Effects (7 papers), Liver Disease Diagnosis and Treatment (6 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (6 papers). Dee Pei collaborates with scholars based in Taiwan, United States and China. Dee Pei's co-authors include Yen-Lin Chen, Chung‐Ze Wu, Hai‐Lun Sun, Ko‐Huang Lue, Chun‐Hsien Hsu, Shi‐Wen Kuo, Shih‐Te Tu, I‐Te Lee, Wayne Huey‐Herng Sheu and Yi-Jen Hung and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Journal of Pediatrics.

In The Last Decade

Dee Pei

22 papers receiving 337 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dee Pei Taiwan 10 183 99 94 94 59 25 347
Amin Roshdy Soliman Egypt 12 111 0.6× 93 0.9× 53 0.6× 54 0.6× 71 1.2× 53 416
Jeffrey C. Sirota United States 9 209 1.1× 77 0.8× 129 1.4× 208 2.2× 83 1.4× 9 467
Fitsum Guebre-Egziabher France 8 176 1.0× 61 0.6× 21 0.2× 123 1.3× 46 0.8× 12 394
Emma Bodenham United Kingdom 2 161 0.9× 71 0.7× 27 0.3× 94 1.0× 19 0.3× 2 291
Sol Carriazo Spain 11 117 0.6× 58 0.6× 25 0.3× 22 0.2× 69 1.2× 18 306
D. S. Han South Korea 10 285 1.6× 38 0.4× 61 0.6× 28 0.3× 55 0.9× 15 419
Leping Shao China 5 99 0.5× 32 0.3× 22 0.2× 93 1.0× 33 0.6× 10 273
Vladimir Dobronravov Russia 8 182 1.0× 27 0.3× 21 0.2× 33 0.4× 64 1.1× 87 333
Raiz Ahmad Misgar India 12 68 0.4× 216 2.2× 28 0.3× 32 0.3× 116 2.0× 35 427
Philippos Kaldrymides Greece 8 58 0.3× 112 1.1× 122 1.3× 45 0.5× 37 0.6× 12 383

Countries citing papers authored by Dee Pei

Since Specialization
Citations

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

Fields of papers citing papers by Dee Pei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dee Pei

This figure shows the co-authorship network connecting the top 25 collaborators of Dee Pei. A scholar is included among the top collaborators of Dee Pei based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dee Pei. Dee Pei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shen, Chih‐Hao, et al.. (2025). Using machine learning methods to investigate the role of volatile organic compounds in non-alcoholic fatty liver disease. Frontiers in Molecular Biosciences. 12. 1631265–1631265.
2.
3.
Chen, I‐Chien, et al.. (2024). Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up. Diagnostics. 14(10). 979–979. 2 indexed citations
4.
Huang, Li-Ying, Fang-Yu Chen, Chun‐Heng Kuo, et al.. (2022). Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study. Journal of Clinical Medicine. 11(13). 3661–3661. 11 indexed citations
5.
Lin, Jiunn‐Diann, Dee Pei, Chung‐Ze Wu, et al.. (2022). Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes. Diagnostics. 12(7). 1619–1619. 3 indexed citations
6.
Wu, Chung‐Ze, Chieh‐Hua Lu, Jiunn‐Diann Lin, et al.. (2020). Relationships between white blood cell count and insulin resistance, glucose effectiveness, and first- and second-phase insulin secretion in young adults. Medicine. 99(43). e22215–e22215. 7 indexed citations
7.
Chang, Junn-Liang, Yi-Jen Hung, Chang‐Hsun Hsieh, et al.. (2016). The role of uric acid for predicting future metabolic syndrome and type 2 diabetes in older people. The journal of nutrition health & aging. 21(3). 329–335. 25 indexed citations
8.
Sun, Hai‐Lun, Dee Pei, Ko‐Huang Lue, & Yen-Lin Chen. (2015). Uric Acid Levels Can Predict Metabolic Syndrome and Hypertension in Adolescents: A 10-Year Longitudinal Study. PLoS ONE. 10(11). e0143786–e0143786. 73 indexed citations
9.
Hsieh, Chang‐Hsun, et al.. (2015). The power of serum uric acid in predicting metabolic syndrome diminishes with age in an elderly Chinese population. The journal of nutrition health & aging. 20(9). 912–917. 13 indexed citations
10.
11.
12.
Wu, Chung‐Ze, Yuh‐Feng Lin, Yi-Jen Hung, et al.. (2015). Urokinase plasminogen activator receptor and its soluble form in common biopsy-proven kidney diseases and in staging of diabetic nephropathy. Clinical Biochemistry. 48(18). 1324–1329. 28 indexed citations
13.
Hsieh, Chang‐Hsun, Jiunn‐Diann Lin, Chung‐Ze Wu, et al.. (2014). Is lower uric acid level better? A combined cross-sectional and longitudinal study in the elderly. Endocrine. 47(3). 806–815. 6 indexed citations
14.
Chao, Ting‐Ting, et al.. (2014). VEGF-D as a Marker in the Aid of Malignant Metastatic Pleural Effusion Diagnosis. Applied immunohistochemistry & molecular morphology. 23(3). 209–214. 3 indexed citations
15.
Lin, Jiunn‐Diann, Chun‐Hsien Hsu, Chung‐Ze Wu, et al.. (2013). Higher uric acid is associated with higher rate of metabolic syndrome in Chinese elderly. European Geriatric Medicine. 5(1). 26–30. 4 indexed citations
16.
Hsiao, Fone‐Ching, et al.. (2012). Thyrotropin as an Independent Factor of Renal Function and Chronic Kidney Disease in Normoglycemic Euthyroid Adults. Endocrine Research. 37(3). 110–116. 30 indexed citations
17.
Pei, Dee, et al.. (2012). Association Between Platelet Count and Components of Metabolic Syndrome in Geriatric Taiwanese Males. SHILAP Revista de lepidopterología. 6(3). 215–219. 12 indexed citations
18.
Chen, Yen-Lin, et al.. (2012). Predictive Value of Serum Uric Acid Levels for the Diagnosis of Metabolic Syndrome in Adolescents. The Journal of Pediatrics. 161(4). 753–756.e2. 42 indexed citations
19.
Ma, Wen‐Ya, Chia‐Chao Wu, Dee Pei, et al.. (2010). Glycated albumin is independently associated with estimated glomerular filtration rate in nondiabetic patients with chronic kidney disease. Clinica Chimica Acta. 412(7-8). 583–586. 10 indexed citations
20.
Lee, I‐Te, Wayne Huey‐Herng Sheu, Shih‐Te Tu, Shi‐Wen Kuo, & Dee Pei. (2006). Bisphosphonate pretreatment attenuates hungry bone syndrome postoperatively in subjects with primary hyperparathyroidism. Journal of Bone and Mineral Metabolism. 24(3). 255–258. 47 indexed citations

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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