Fang‐Yu Lin

37 papers receiving 789 citations

Peers

Fang‐Yu Lin
Comparison fields: 5 of 116
  • Biochemistry 79
  • Public Health, Environmental and Occupational Health 288
  • Oncology 212
  • Nutrition and Dietetics 95
  • Rheumatology 90
Replace Archana J. McEligot with:
Archana J. McEligot United States
Torukiri I Ibiebele Australia
Rudolph J. J. Hermus Netherlands
E. A. Platz United States
Tapashi Dalvi United States
Rikke Egeberg Denmark
Sanjeev Budhathoki Japan
Maroun Bou Zerdan United States
Cláudia Braga Italy
Anna Danielewicz Poland
Fang‐Yu Lin relative to Archana J. McEligot United States Archana J. McEligot's profile →
Citations per field
00.5×3.8×
Archana J. McEligot · 1×
Citations per year

Countries citing papers authored by Fang‐Yu Lin

Since Specialization
Citations

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

Fields of papers citing papers by Fang‐Yu Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fang‐Yu Lin. 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 Fang‐Yu Lin. The network helps show where Fang‐Yu Lin may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009163
2 201266
3 200951
4 201049
5 202048
6 201736
7 201135
8 201735
9 201833
10 200931
11 201930
12 201927
13 201023
14 201520
15 202317
16 202316
17 201616
18 202314
19 201614
20 201112

About Fang‐Yu Lin

Fang‐Yu Lin is a scholar working on Public Health, Environmental and Occupational Health, Oncology, Molecular Biology, Pulmonary and Respiratory Medicine and Rheumatology, having authored 39 papers that have together received 816 indexed citations. Recurring topics across this work include Nutritional Studies and Diet (14 papers), Cancer Risks and Factors (7 papers), Folate and B Vitamins Research (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Phytoestrogen effects and research (3 papers), Diet and metabolism studies (3 papers), COVID-19 diagnosis using AI (2 papers) and Glaucoma and retinal disorders (2 papers). The work is most often cited by research in Biochemistry (79 citations), Public Health, Environmental and Occupational Health (288 citations), Oncology (212 citations), Nutrition and Dietetics (95 citations) and Rheumatology (90 citations). Fang‐Yu Lin has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Cai‐Xia Zhang, Suzanne C. Ho, Yu‐Ming Chen, Jianhua Fu, Shouzhen Cheng, Xiong‐Fei Mo, Ching‐Hsing Wang, Wuqing Huang, Jing Huang and Naiqi Zhang. Their work appears in journals such as British Journal Of Nutrition, Scientific Reports, Cancer, European Journal of Cancer Prevention and IEEE Access.

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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