Yinlian Wu

2.5k citations
28 papers · 1.6k indexed · 2 hit papers · h-index 15

Yinlian Wu

26 papers receiving 1.6k citations

Hit Papers

Comparison of MAFLD and NAFLD diagnostic criteria in real...3892020202620222024100200300400

Peers

Yinlian Wu
Comparison fields: 5 of 118
  • Infectious Diseases 649
  • Hepatology 261
  • Neurology 326
  • Epidemiology 714
  • Endocrinology, Diabetes and Metabolism 246
Replace Jiaofeng Huang with:
Jiaofeng Huang China
Rahul Kumar Singapore
Jingfeng Bi China
Biyao Zou United States
Vijay Gayam United States
Bálint Erőss Hungary
Naresh Bansal India
Markos Kalligeros United States
Xiaowei Xu China
Perica Davitkov United States
Yinlian Wu relative to Jiaofeng Huang China Jiaofeng Huang's profile →
Citations per field
00.5×1.5×
Jiaofeng Huang · 1×
Citations per year

Countries citing papers authored by Yinlian Wu

Since Specialization
Citations

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

Fields of papers citing papers by Yinlian Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20251
3 20240
4 20246
5 202411
6 202329
7 20223
8 202215
9 202215
10 20226
11 202125
12 202139
13 202116
14 202115
15 2020309
16 202015
17 202089
18 20193
19 201848
20 201624

About Yinlian Wu

Yinlian Wu is a scholar working on Hepatology, Epidemiology, Parasitology, Endocrinology, Diabetes and Metabolism and Pathology and Forensic Medicine, having authored 28 papers that have together received 1.6k indexed citations. Recurring topics across this work include Liver Disease Diagnosis and Treatment (18 papers), Liver Disease and Transplantation (8 papers), Hepatitis B Virus Studies (5 papers), Hepatitis C virus research (5 papers), COVID-19 Clinical Research Studies (3 papers), Diet, Metabolism, and Disease (3 papers), Alcohol Consumption and Health Effects (3 papers) and Vitamin D Research Studies (2 papers). The work is most often cited by research in Infectious Diseases (649 citations), Hepatology (261 citations), Neurology (326 citations), Epidemiology (714 citations) and Endocrinology, Diabetes and Metabolism (246 citations). Yinlian Wu has collaborated with scholars based in China, Singapore and United Kingdom. Frequent co-authors include Jiaofeng Huang, Rahul Kumar, Su Lin, Qianwen Zhao, Yunlei Deng, Ming-Fang Wang, Yueyong Zhu, Ningfang Lian, Shiying Liu and Xiaozhong Wang. Their work appears in journals such as Journal of Medical Virology, Scientific Reports, World Journal of Gastroenterology, Liver International and International Journal of Medical Sciences.

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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