Di You

839 citations
31 papers · 478 · h-index 9

Impact in

Papers in

Di You

29 papers receiving 466 citations

Peers

Di You
Comparison fields: 5 of 98
  • Computer Vision and Pattern Recognition 234
  • Media Technology 37
  • Artificial Intelligence 110
  • Aging 5
  • Transportation 17
Replace Xianyong Fang with:
Xianyong Fang China
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Fangfang Li China
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Citations per field
00.5×4.3×
Xianyong Fang · 1×
Citations per year

Countries citing papers authored by Di You

Since Specialization
Citations

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

Fields of papers citing papers by Di You

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201392
2 202182
3 201074
4 202253
5 201339
6 201924
7 202223
8 202215
9 201011
10 20218
11 20228
12 20188
13 20228
14 20215
15
Association between single nucleotide polymorphisms in the programmed cell death 6 gene and the risk of endometrial cancer in Chinese Han women.
20173
16
Transcription factor FOXP3 gene variants affect epithelial ovarian carcinoma in the Han Chinese population.
20183
17
Interleukin-31 single nucleotide polymorphisms are significantly associated with endometrial cancer in Chinese Han women.
20183
18
Model Selection in Kernel Methods
20112
19 20242
20 20232

About Di You

Di You is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology, Media Technology, Artificial Intelligence and Reproductive Medicine, having authored 31 papers that have together received 478 indexed citations. Recurring topics across this work include Image Processing Techniques and Applications (6 papers), Optical measurement and interference techniques (6 papers), Advanced Measurement and Metrology Techniques (4 papers), Ovarian cancer diagnosis and treatment (4 papers), Face and Expression Recognition (3 papers), Cancer-related molecular mechanisms research (3 papers), Spectroscopy and Chemometric Analyses (2 papers) and Facility Location and Emergency Management (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (234 citations), Media Technology (37 citations), Artificial Intelligence (110 citations), Aging (5 citations) and Transportation (17 citations). Di You has collaborated with scholars based in China, United States and Vietnam. Frequent co-authors include Aleix M. Martı́nez, Onur C. Hamsici, Martin Griss, Jianguo Li, Heng-Tze Cheng, Feng-Tso Sun, Jiangping Zhu, Lin Zhang, Qiuyun Yang and Pei Zhou. Their work appears in journals such as Optics Express, Remote Sensing, Medicine, Applied Optics and Optical Engineering.

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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