Shuli Yang

924 citations
34 papers · 616 indexed · h-index 14

Impact in

Papers in

    • Circular RNAs in diseases 5
    • RNA modifications and cancer 2
    • Cancer-related molecular mechanisms research 6
    • MicroRNA in disease regulation 5
    • Cancer, Lipids, and Metabolism 2

Shuli Yang

32 papers receiving 601 citations

Peers

Shuli Yang
Comparison fields: 5 of 85
  • Cancer Research 162
  • Obstetrics and Gynecology 48
  • Pharmacology 51
  • Oncology 129
  • Immunology 99
Replace Di Xiao with:
Di Xiao China
Hyung-Lae Lee South Korea
Yanying Xu China
Slavica Stojnev Serbia
Hongping Wang China
Liviuţa Budişan Romania
Huixiang Yang China
Jianjun Liu China
Swayamjot Kaur United States
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Citations per field
00.5×2.8×
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Citations per year

Countries citing papers authored by Shuli Yang

Since Specialization
Citations

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

Fields of papers citing papers by Shuli Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016157
2 201655
3 201852
4
Kaempferol exerts anti-proliferative effects on human ovarian cancer cells by inducing apoptosis, G0/G1 cell cycle arrest and modulation of MEK/ERK and STAT3 pathways.
202037
5 202133
6 202325
7 202025
8 201423
9 201922
10 201120
11 202017
12 201916
13 201814
14 202113
15 202313
16 202013
17
MicroRNA-375 inhibits the growth, drug sensitivity and metastasis of human ovarian cancer cells by targeting PAX2.
202012
18 201910
19 20249
20 20199

About Shuli Yang

Shuli Yang is a scholar working on Molecular Biology, Cancer Research, Oncology, Pulmonary and Respiratory Medicine and Surgery, having authored 34 papers that have together received 616 indexed citations. Recurring topics across this work include Cancer-related molecular mechanisms research (6 papers), Circular RNAs in diseases (5 papers), MicroRNA in disease regulation (5 papers), Endometriosis Research and Treatment (3 papers), Cancer, Lipids, and Metabolism (2 papers), Cancer Cells and Metastasis (2 papers), RNA modifications and cancer (2 papers) and Cancer Mechanisms and Therapy (2 papers). The work is most often cited by research in Cancer Research (162 citations), Obstetrics and Gynecology (48 citations), Pharmacology (51 citations), Oncology (129 citations) and Immunology (99 citations). Shuli Yang has collaborated with scholars based in China, Taiwan and United States. Frequent co-authors include Ruixin Lin, Lihui Si, Ruiqi Yang, Yan Jia, Jiacheng Wu, Zongbing You, Keshab R. Parajuli, Shuang Liu, Leann Myers and Ting Li. Their work appears in journals such as Bioscience Reports, Frontiers in Endocrinology, Microbial Pathogenesis, European Journal of Medicinal Chemistry and Journal of Microbiology Immunology and Infection.

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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