Su An

900 citations
35 papers · 656 · h-index 15

Impact in

    • Receptor Mechanisms and Signaling
    • RNA modifications and cancer
    • PI3K/AKT/mTOR signaling in cancer
    • Melanoma and MAPK Pathways
    • RNA Research and Splicing
    • Protein Kinase Regulation and GTPase Signaling

Papers in

Su An

35 papers receiving 654 citations

Peers

Su An
Comparison fields: 5 of 91
  • Molecular Biology 437
  • Cancer Research 74
  • Immunology and Allergy 26
  • Cellular and Molecular Neuroscience 66
  • Oncology 97
Replace Shengchen Lin with:
Shengchen Lin China
Kum-Joo Shin South Korea
Chung-Ming Lin Taiwan
Xiping Zhang China
Eiji Kobayashi Japan
Christopher D. Kane United States
Marissa J. Nadolski United States
Beth A. Strifler United States
Nai-Sum Wong Hong Kong
Su An relative to Shengchen Lin China Shengchen Lin's profile →
Citations per field
00.5×1.5×1.9×
Shengchen Lin · 1×
Citations per year

Countries citing papers authored by Su An

Since Specialization
Citations

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

Fields of papers citing papers by Su An

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201699
2 202073
3 201154
4 201249
5 202147
6 201747
7 201528
8 202126
9 202118
10 201817
11 201817
12 201517
13 201616
14 201416
15 201815
16 202411
17 201811
18 202110
19 202410
20 202310

About Su An

Su An is a scholar working on Molecular Biology, Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Pathology and Forensic Medicine and Oncology, having authored 35 papers that have together received 656 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Melanoma and MAPK Pathways (8 papers), Receptor Mechanisms and Signaling (8 papers), Protein Kinase Regulation and GTPase Signaling (7 papers), Cancer Mechanisms and Therapy (5 papers), Neuropeptides and Animal Physiology (4 papers), PI3K/AKT/mTOR signaling in cancer (4 papers) and Circadian rhythm and melatonin (3 papers). The work is most often cited by research in Molecular Biology (437 citations), Cancer Research (74 citations), Immunology and Allergy (26 citations), Cellular and Molecular Neuroscience (66 citations) and Oncology (97 citations). Su An has collaborated with scholars based in China, United Kingdom and South Korea. Frequent co-authors include Tian‐Rui Xu, Xiao‐Xi Guo, Richard J. Ward, Ying Liu, Yang Yang, Qian Hao, Jiaojiao Yu, Yang Yang, Ying Liu and Yin Liu. Their work appears in journals such as FEBS Journal, Biochemical Pharmacology, Biochemical and Biophysical Research Communications, Journal of Pineal Research and Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics.

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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