Bing Ji

109 papers receiving 1.9k citations

Peers

Bing Ji
Comparison fields: 5 of 150
  • Computer Vision and Pattern Recognition 286
  • Biological Psychiatry 29
  • Health Informatics 11
  • Cancer Research 104
  • Radiology, Nuclear Medicine and Imaging 159
Replace Xiong Zhang with:
Xiong Zhang China
Jianwei Lu China
Guangzhi Wang China
Tingting Jiang China
Xiuli Li China
Michael Wainberg Canada
Ryo Watanabe Japan
Min Du China
Jing Qin China
Yi Huang China
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Citations per field
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Citations per year

Countries citing papers authored by Bing Ji

Since Specialization
Citations

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

Fields of papers citing papers by Bing Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 123 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014247
2 2020134
3 2017100
4 202180
5 201759
6
FOXC1 promotes proliferation and epithelial-mesenchymal transition in cervical carcinoma through the PI3K-AKT signal pathway.
201753
7 201952
8 201550
9 201949
10 201147
11 201847
12 202047
13 201942
14 201840
15 201639
16
Tissue of origin dictates branched-chain amino acid metabolism in mutant Kras-driven cancers
201636
17 202234
18 202034
19 201633
20 202132

About Bing Ji

Bing Ji is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Oncology and Artificial Intelligence, having authored 123 papers that have together received 2.0k indexed citations. Recurring topics across this work include Biomimetic flight and propulsion mechanisms (7 papers), Bone health and treatments (6 papers), Functional Brain Connectivity Studies (5 papers), Advanced Neural Network Applications (4 papers), Multiple Myeloma Research and Treatments (4 papers), EEG and Brain-Computer Interfaces (4 papers), Advanced MRI Techniques and Applications (4 papers) and Bone health and osteoporosis research (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (286 citations), Biological Psychiatry (29 citations), Health Informatics (11 citations), Cancer Research (104 citations) and Radiology, Nuclear Medicine and Imaging (159 citations). Bing Ji has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Yibin Li, Xin Ma, Haibo Wang, Menghua Zhang, Xingong Cheng, Yongfeng Zhang, Chuan Zhang, Yueming Sun, Shenglin Xiong and Yongling An. Their work appears in journals such as Computer Methods and Programs in Biomedicine, Oncology Reports, Computers in Biology and Medicine, Aerospace Science and Technology and IEEE Transactions on Medical Imaging.

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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