Long Wang

3.3k citations
105 papers · 2.4k · h-index 26

Impact in

  • Physiology top 1%
    • Adenosine and Purinergic Signaling
  • Oncology top 5%
    • Cancer Cells and Metastasis
    • Cancer Immunotherapy and Biomarkers

Papers in

    • RNA modifications and cancer 8
    • TGF-β signaling in diseases 6
    • Cancer-related gene regulation 5
    • Bone Metabolism and Diseases 4
    • Peptidase Inhibition and Analysis 5
    • Cancer-related Molecular Pathways 4

Long Wang

96 papers receiving 2.4k citations

Peers

Long Wang
Comparison fields: 5 of 112
  • Physiology 234
  • Oncology 659
  • Cancer Research 260
  • Molecular Biology 1.2k
  • Immunology 334
Replace Beixue Gao with:
Beixue Gao United States
Alexandre Puissant France
Nelson S. Yee United States
Xiuling Zhi China
S. Calandra Italy
Ronald Barbaras France
Cong Yan United States
Motoi Ohba Japan
Hayato Hikita Japan
Long Wang relative to Beixue Gao United States Beixue Gao's profile →
Citations per field
00.5×1.5×1.9×
Beixue Gao · 1×
Citations per year

Countries citing papers authored by Long Wang

Since Specialization
Citations

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

Fields of papers citing papers by Long Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 105 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010349
2 2010175
3 2001174
4 2006156
5 2004141
6 200885
7 200683
8 201969
9 200961
10 201359
11 200458
12 201549
13 201441
14 201939
15 201438
16 201538
17 201236
18 202434
19 201833
20 201832

About Long Wang

Long Wang is a scholar working on Molecular Biology, Oncology, Cancer Research, Epidemiology and Pulmonary and Respiratory Medicine, having authored 105 papers that have together received 2.4k indexed citations. Recurring topics across this work include RNA modifications and cancer (8 papers), TGF-β signaling in diseases (6 papers), Cancer-related gene regulation (5 papers), MicroRNA in disease regulation (5 papers), Peptidase Inhibition and Analysis (5 papers), Bone Metabolism and Diseases (4 papers), Cancer-related Molecular Pathways (4 papers) and Vasculitis and related conditions (3 papers). The work is most often cited by research in Physiology (234 citations), Oncology (659 citations), Cancer Research (260 citations), Molecular Biology (1.2k citations) and Immunology (334 citations). Long Wang has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Lu‐Zhe Sun, Abhik Bandyopadhyay, Joseph K. Agyin, Yuping Tang, Bin Zhang, Tahiro Shin, Dachuan Jin, Tyler J. Curiel, Aijie Liu and Linda F. Thompson. Their work appears in journals such as Cancer Research, Journal of Clinical Oncology, Medicine, The Prostate and Blood.

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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