Da‐Zhi Chen

76 papers receiving 1.7k citations

Peers

Da‐Zhi Chen
Comparison fields: 5 of 119
  • Hepatology 267
  • Toxicology 77
  • Cancer Research 224
  • Genetics 108
  • Epidemiology 336
Replace Qinjie Weng with:
Qinjie Weng China
Yoshinori Otsuki Japan
Rongxue Wu United States
Hongwei Lü China
Xuqi Li China
Lin Qiu China
Xiaofei Li China
Shizhong Bu China
Zhi Yang China
Da‐Zhi Chen relative to Qinjie Weng China Qinjie Weng's profile →
Citations per field
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Qinjie Weng · 1×
Citations per year

Countries citing papers authored by Da‐Zhi Chen

Since Specialization
Citations

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

Fields of papers citing papers by Da‐Zhi Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Da‐Zhi Chen. 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 Da‐Zhi Chen. The network helps show where Da‐Zhi Chen may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017195
2
Indole-3-carbinol prevents cervical cancer in human papilloma virus type 16 (HPV16) transgenic mice.
1999116
3 2018111
4 2000109
5 200196
6 201490
7 201878
8 201962
9 202149
10 199641
11 200541
12
Indocyanine green clearance test and model for end-stage liver disease score of patients with liver cirrhosis.
200940
13 199436
14 201732
15 199429
16 201928
17 201827
18 202326
19 202125
20 201623

About Da‐Zhi Chen

Da‐Zhi Chen is a scholar working on Epidemiology, Hepatology, Molecular Biology, Surgery and Physiology, having authored 81 papers that have together received 1.7k indexed citations. Recurring topics across this work include Liver Disease Diagnosis and Treatment (18 papers), Liver Diseases and Immunity (12 papers), Neuroscience and Neuropharmacology Research (8 papers), Organ Transplantation Techniques and Outcomes (6 papers), Alcohol Consumption and Health Effects (5 papers), Nitric Oxide and Endothelin Effects (4 papers), Hepatitis B Virus Studies (4 papers) and Biochemical effects in animals (4 papers). The work is most often cited by research in Hepatology (267 citations), Toxicology (77 citations), Cancer Research (224 citations), Genetics (108 citations) and Epidemiology (336 citations). Da‐Zhi Chen has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Lanman Xu, Karen J. Auborn, Yongping Chen, Mei Qi, En-De Hu, Jinlu Wu, Feng‐Bin Lu, Seitaro Ohkuma, Zhuo Lin and Yuewen Gong. Their work appears in journals such as World Journal of Gastroenterology, Journal of Cellular and Molecular Medicine, International Immunopharmacology, Journal of Gastroenterology and Hepatology and Journal of Iron and Steel Research International.

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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