Yayun Ren

548 total citations
35 papers, 289 citations indexed

About

Yayun Ren is a scholar working on Epidemiology, Hepatology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yayun Ren has authored 35 papers receiving a total of 289 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Epidemiology, 18 papers in Hepatology and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yayun Ren's work include Liver Disease Diagnosis and Treatment (22 papers), Hepatocellular Carcinoma Treatment and Prognosis (9 papers) and Liver Disease and Transplantation (7 papers). Yayun Ren is often cited by papers focused on Liver Disease Diagnosis and Treatment (22 papers), Hepatocellular Carcinoma Treatment and Prognosis (9 papers) and Liver Disease and Transplantation (7 papers). Yayun Ren collaborates with scholars based in China, United States and Singapore. Yayun Ren's co-authors include Dean Tai, Shuhui Jiang, Xueming Qian, Elaine Chng, Arun J. Sanyal, Nikolai V. Naoumov, Dominique Brees, Juergen Loeffler, Patricia López and Feng Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer Research and Scientific Reports.

In The Last Decade

Yayun Ren

34 papers receiving 286 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yayun Ren China 9 168 120 56 43 39 35 289
P.S. Zoumpoulis Greece 11 208 1.2× 155 1.3× 105 1.9× 26 0.6× 51 1.3× 29 403
Ioannis Theotokas Greece 9 158 0.9× 127 1.1× 65 1.2× 22 0.5× 17 0.4× 22 307
Barbara Rosado Austria 3 240 1.4× 128 1.1× 42 0.8× 85 2.0× 6 0.2× 4 346
Adélia Simão Portugal 6 58 0.3× 43 0.4× 94 1.7× 23 0.5× 9 0.2× 23 275
Peter Henstock United States 8 36 0.2× 112 0.9× 27 0.5× 73 1.7× 43 1.1× 12 571
Yuanxiang Gao China 11 27 0.2× 88 0.7× 63 1.1× 72 1.7× 28 0.7× 23 311
Marina Gorunescu Romania 10 44 0.3× 16 0.1× 130 2.3× 160 3.7× 28 0.7× 19 406

Countries citing papers authored by Yayun Ren

Since Specialization
Citations

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

Fields of papers citing papers by Yayun Ren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yayun Ren

This figure shows the co-authorship network connecting the top 25 collaborators of Yayun Ren. A scholar is included among the top collaborators of Yayun Ren 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 Yayun Ren. Yayun Ren 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.
Ren, Yayun, Jenghwa Chang, Dean Tai, et al.. (2025). Repeatability and Reproducibility of Artificial Imaging-based Digital Pathology for the Evaluation of Liver Fibrosis. Clinical Gastroenterology and Hepatology. 24(3). 713–722. 1 indexed citations
2.
Sun, Xin, Zhimin Zhao, Qiang Yang, et al.. (2025). A machine learning based algorithm accurately stages liver disease by quantification of arteries. Scientific Reports. 15(1). 3143–3143. 3 indexed citations
3.
Sun, Dan‐Qin, Jiaqi Shen, Yayun Ren, et al.. (2025). Liver fibrosis progression analyzed with AI predicts renal decline. JHEP Reports. 7(5). 101358–101358. 1 indexed citations
4.
Liu, Feng, Yameng Sun, Dean Tai, et al.. (2024). AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response. Diagnostics. 14(16). 1837–1837. 2 indexed citations
5.
Sun, Dan‐Qin, Jiaqi Shen, Yangyang Li, et al.. (2024). FRI-267 Regional fibrosis progression analysed by digital pathology with artificial intelligence is associated with renal dysfunction. Journal of Hepatology. 80. S488–S489. 1 indexed citations
6.
Kendall, Timothy J., Elaine Chng, Yayun Ren, et al.. (2024). Outcome prediction in metabolic dysfunction‐associated steatotic liver disease using stain‐free digital pathological assessment. Liver International. 44(10). 2511–2516. 3 indexed citations
7.
Naoumov, Nikolai V., David E. Kleiner, Elaine Chng, et al.. (2024). Digital quantitation of bridging fibrosis and septa reveals changes in natural history and treatment not seen with conventional histology. Liver International. 44(12). 3214–3228. 4 indexed citations
8.
9.
Ren, Yayun, Yangyang Li, Xinlei Wang, et al.. (2024). AI‐based digital pathology provides newer insights into lifestyle intervention‐induced fibrosis regression in MASLD: An exploratory study. Liver International. 44(10). 2572–2582. 8 indexed citations
10.
Sun, Yameng, Qianyi Wang, Xinyan Zhao, et al.. (2024). Delicate and thin fibrous septa indicate a regression tendency in metabolic dysfunction-associated steatohepatitis patients with advanced fibrosis. Hepatology International. 19(1). 166–180. 1 indexed citations
11.
Noureddin, Mazen, Zachary Goodman, Dean Tai, et al.. (2023). Machine learning liver histology scores correlate with portal hypertension assessments in nonalcoholic steatohepatitis cirrhosis. Alimentary Pharmacology & Therapeutics. 57(4). 409–417. 13 indexed citations
12.
Naoumov, Nikolai V., Dominique Brees, Juergen Loeffler, et al.. (2022). Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH. Journal of Hepatology. 77(5). 1399–1409. 72 indexed citations
13.
Noureddin, Mazen, Dean Tai, Elaine Chng, et al.. (2022). Derivation of machine learning histologic scores correlating with portal pressures and the development of varices in NASH patients with cirrhosis. Journal of Hepatology. 77. S623–S624. 1 indexed citations
14.
Liu, Feng, Lai Wei, Wei Qiang Leow, et al.. (2022). Developing a New qFIBS Model Assessing Histological Features in Pediatric Patients With Non-alcoholic Steatohepatitis. Frontiers in Medicine. 9(6). 3–8. 4 indexed citations
15.
Leow, Wei Qiang, Pierre Bédossa, Feng Liu, et al.. (2020). An Improved qFibrosis Algorithm for Precise Screening and Enrollment into Non-Alcoholic Steatohepatitis (NASH) Clinical Trials. Diagnostics. 10(9). 643–643. 14 indexed citations
16.
Wang, Bingqiong, Yameng Sun, Jialing Zhou, et al.. (2019). SHG/TPEF-based image technology improves liver fibrosis assessment of minimally sized needle biopsies. Hepatology International. 13(4). 501–509. 11 indexed citations
17.
Qian, Xueming, et al.. (2018). Social media based event summarization by user–text–image co-clustering. Knowledge-Based Systems. 164. 107–121. 38 indexed citations
18.
19.
Ren, Yayun, Xueming Qian, & Shuhui Jiang. (2015). Visual summarization for place-of-interest by social-contextual constrained geo-clustering. 1–6. 6 indexed citations
20.
Ren, Yayun, et al.. (2015). Multi-task ant system for multi-object parameter estimation and its application in cell tracking. Applied Soft Computing. 35. 449–469. 5 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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