Jun Shen

276 papers receiving 7.6k citations

Jun Shen's Hit Papers

MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer 2023 · 138 citations
1380+2+4Years since publication100200300400500

Peers

Jun Shen
Comparison fields: 5 of 171
  • Health Informatics 108
  • Biomaterials 1.0k
  • Radiology, Nuclear Medicine and Imaging 1.3k
  • Biomedical Engineering 1.8k
  • Inorganic Chemistry 558
Replace Pengfei Rong with:
Pengfei Rong China
Hui Wang China
Kiran Kalia India
Mei Tian China
Sung Jun Kim South Korea
Xuelei Ma China
Kui Luo China
Rongrong Zhu China
Jiansong Ji China
Michael Schäfers Germany
Jun Shen relative to Pengfei Rong China Pengfei Rong's profile →
Citations per field
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Pengfei Rong · 1×
Citations per year

Countries citing papers authored by Jun Shen

Since Specialization
Citations

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

Fields of papers citing papers by Jun Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 291 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Hit paper breakdown →
2021531
2
“Armor‐Plating” Enzymes with Metal–Organic Frameworks (MOFs)
Hit paper breakdown →
2020362
3 2018306
4 2019224
5 2018181
6 2020151
7 2020150
8 2021145
9
MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer
Hit paper breakdown →
2023138
10 2017128
11 2019128
12 2019117
13 2020107
14 2019101
15 201996
16 202095
17 202183
18 200879
19 201978
20 201077

About Jun Shen

Jun Shen is a scholar working on Biomedical Engineering, Molecular Biology, Radiology, Nuclear Medicine and Imaging, Biomaterials and Surgery, having authored 291 papers that have together received 7.6k indexed citations. Recurring topics across this work include Nanoplatforms for cancer theranostics (27 papers), Nanoparticle-Based Drug Delivery (23 papers), RNA Interference and Gene Delivery (16 papers), Radiomics and Machine Learning in Medical Imaging (16 papers), Nerve injury and regeneration (10 papers), MRI in cancer diagnosis (9 papers), Metal-Organic Frameworks: Synthesis and Applications (7 papers) and Advanced biosensing and bioanalysis techniques (7 papers). The work is most often cited by research in Health Informatics (108 citations), Biomaterials (1.0k citations), Radiology, Nuclear Medicine and Imaging (1.3k citations), Biomedical Engineering (1.8k citations) and Inorganic Chemistry (558 citations). Jun Shen has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Xiaohui Duan, Siming Huang, Gangfeng Ouyang, Guosheng Chen, Xiaoxue Kou, Fang Zhang, Xintao Shuai, Jiaji Mao, Liejing Lu and Shuqi Jiang. Their work appears in journals such as European Radiology, European Journal of Radiology, Journal of Magnetic Resonance Imaging, Frontiers in Oncology and ACS Applied Materials & Interfaces.

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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