Feng Jin

3.2k citations
103 papers · 1.4k · h-index 21

Impact in

    • Cancer-related molecular mechanisms research
  • Demography top 5%
    • Retirement, Disability, and Employment

Papers in

    • Epigenetics and DNA Methylation 7
    • RNA modifications and cancer 5
    • Cancer-related gene regulation 5
    • Breast Cancer Treatment Studies 8
    • Cancer-related molecular mechanisms research 6

Feng Jin

89 papers receiving 1.3k citations

Peers

Feng Jin
Comparison fields: 5 of 144
  • Cancer Research 225
  • Demography 134
  • Health 94
  • General Health Professions 249
  • Oncology 263
Replace Jennifer Cullen with:
Jennifer Cullen United States
Stephen P. Dunn United States
Lynn Martin Canada
Suzy Kim South Korea
Steven Lehrer United States
Masaru Sasaki Japan
Daniel Francis Australia
Qingjie Xia China
Devendra Raj Singh Nepal
Karen A. Duncan United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Feng Jin

Since Specialization
Citations

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

Fields of papers citing papers by Feng Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 103 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019106
2 2020102
3 201191
4 201974
5 201958
6 201449
7 202043
8 202034
9 202232
10 201831
11 201930
12 201728
13 201526
14 201326
15 201424
16 201923
17 201823
18 201923
19 200822
20 201122

About Feng Jin

Feng Jin is a scholar working on Molecular Biology, Cancer Research, Oncology, Surgery and Sociology and Political Science, having authored 103 papers that have together received 1.4k indexed citations. Recurring topics across this work include Global Health Care Issues (8 papers), Breast Cancer Treatment Studies (8 papers), Epigenetics and DNA Methylation (7 papers), Intergenerational Family Dynamics and Caregiving (7 papers), Cancer-related molecular mechanisms research (6 papers), RNA modifications and cancer (5 papers), Cancer-related gene regulation (5 papers) and Retirement, Disability, and Employment (5 papers). The work is most often cited by research in Cancer Research (225 citations), Demography (134 citations), Health (94 citations), General Health Professions (249 citations) and Oncology (263 citations). Feng Jin has collaborated with scholars based in China, United States and Finland. Frequent co-authors include Xiaoyun Mao, Zhen Wang, Hiroshi Satō, Ang Zheng, Minjie Wei, Ling Wang, Caigang Liu, Qin Li, James P. Smith and Xinyue Song. Their work appears in journals such as Tumor Biology, Frontiers in Oncology, BMC Cancer, Cancer Medicine and China Economic Review.

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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