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Researcher · Beijing Academy of Artificial Intelligence · China

Nanqing Dong

Nanqing Dong publishes mostly in Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition, on topics such as COVID-19 diagnosis using AI, Domain Adaptation and Few-Shot Learning and Machine Learning in Bioinformatics.

ORCID 0000-0001-5014-1993 OpenAlex
World rank
#320,816
of 7,710,434 ranked researchers
Rank in China
#35,925
of 978,568
Works
81
Citations
2.1k
Citations per work
26.1
h-index
22
i10-index 26Top 10% in Artificial Intelligence
Nanqing Dong

What does Nanqing Dong research?

Nanqing Dong's work is mostly in Computer Science (38%), Biochemistry, Genetics and Molecular Biology (19%), Medicine (12%).

Physical Sciences: 34 works · 41.7% of their workComputer Science: 30 works · 37.5% of their workArtificial Intelligence: 19 works · 23.6% of their workComputer Vision and Pattern Recognition: 11 works · 13.9% of their workEngineering: 3 works · 4.2% of their workBiomedical Engineering: 3 works · 4.2% of their workLife Sciences: 20 works · 24.1% of their workBiochemistry, Genetics and Molecular Biology: 15 works · 19.0% of their workMolecular Biology: 15 works · 19.0% of their workAgricultural and Biological Sciences: 4 works · 5.1% of their workPlant Science: 4 works · 5.1% of their workEverything else: 18 works · 22.2% of their workHealth Sciences: 10 works · 12.0% of their workMedicine: 10 works · 12.0% of their workRadiology, Nuclear Medicine and Imaging: 10 works · 12.0% of their workTHEIR WORKNanqing Dong81works
Life Sciences24%Physical Sciences42%Health Sciences12%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science3037.5%
    2. Engineering34.2%
    1. Biochemistry, Genetics and Molecular Biology1519.0%
    2. Agricultural and Biological Sciences45.1%
  3. Everything else1822.2%
    1. Medicine1012.0%
  1. 1Artificial Intelligence24% of their works
  2. 2Molecular Biology19% of their works
  3. 3Computer Vision and Pattern Recognition14% of their works
  4. 4Radiology, Nuclear Medicine and Imaging12% of their works
  5. 5Plant Science5% of their works
  6. 6Biomedical Engineering4% of their works

Research topics

Which keywords describe Nanqing Dong's research?

The keywords of their largest research topics: Transfer Learning, Machine Learning, Medical Imaging, Convolutional Neural Networks, Deep Learning, Chest X-Ray, COVID-19 and Domain Adaptation.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 21 words, with their numbers
  1. Transfer Learning292 topics
  2. Machine Learning243 topics
  3. Medical Imaging222 topics
  4. Convolutional Neural Networks212 topics
  5. Deep Learning212 topics
  6. COVID-19151 topic
  7. Chest X-Ray151 topic
  8. Domain Adaptation141 topic
  9. Few-Shot Learning141 topic
  10. Protein101 topic
  11. Subcellular Localization101 topic
  12. Differential Privacy71 topic
  13. Federated Learning71 topic
  14. Quantitative Analysis71 topic
  15. Radiomics71 topic
  16. Image Recognition61 topic
  17. Object Detection61 topic
  18. Ribosome61 topic
  19. Translation61 topic
  20. RNA-Seq51 topic
  21. sequence alignment51 topic

Where does Nanqing Dong work?

Nanqing Dong's main affiliation in the publication record is Beijing Academy of Artificial Intelligence, China.

How many publications and citations does Nanqing Dong have?

OpenAlex counts 81 works and 2,116 citations for Nanqing Dong, an h-index of 22, ranking #320,816 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

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