Dinghong Wu

461 citations
17 papers · 365 · h-index 8

Impact in

Papers in

    • Machine Learning in Bioinformatics 1
    • Psoriasis: Treatment and Pathogenesis 5
    • T-cell and B-cell Immunology 2

Dinghong Wu

16 papers receiving 358 citations

Peers

Dinghong Wu
Comparison fields: 5 of 69
  • Complementary and alternative medicine 112
  • Dermatology 65
  • Immunology 128
  • Pharmacology 45
  • Analytical Chemistry 30
Replace Jian‐Hong Chu with:
Jian‐Hong Chu Hong Kong
Kaixian Chen China
Young‐Rak Cho South Korea
Wan-Lin Chang China
Soon Sung Lim South Korea
Yong‐Zhan Zhen China
Kyoung Jin Nho South Korea
Ha-Rim Kim South Korea
Cheng‐Le Yin Hong Kong
Jongmin Ahn South Korea
Dinghong Wu relative to Jian‐Hong Chu Hong Kong Jian‐Hong Chu's profile →
Citations per field
00.5×3.6×
Jian‐Hong Chu · 1×
Citations per year

Countries citing papers authored by Dinghong Wu

Since Specialization
Citations

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

Fields of papers citing papers by Dinghong Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 2005112
2 201778
3 201945
4 202141
5 201824
6 202116
7 201610
8 20227
9 20237
10 20245
11 20225
12 20235
13 20113
14 20203
15 20092
16 20112
17 20230

About Dinghong Wu

Dinghong Wu is a scholar working on Molecular Biology, Immunology, Dermatology, Complementary and alternative medicine and Computational Theory and Mathematics, having authored 17 papers that have together received 365 indexed citations. Recurring topics across this work include Psoriasis: Treatment and Pathogenesis (5 papers), Dermatology and Skin Diseases (4 papers), Traditional Chinese Medicine Analysis (4 papers), Computational Drug Discovery Methods (3 papers), Cytokine Signaling Pathways and Interactions (2 papers), T-cell and B-cell Immunology (2 papers), Flavonoids in Medical Research (2 papers) and Machine Learning in Bioinformatics (1 paper). The work is most often cited by research in Complementary and alternative medicine (112 citations), Dermatology (65 citations), Immunology (128 citations), Pharmacology (45 citations) and Analytical Chemistry (30 citations). Dinghong Wu has collaborated with scholars based in China, Taiwan and Macao. Frequent co-authors include Chuanjian Lu, Xi Tang, Weiwei Su, Liwei Yang, Xiaorui Wang, Yan Ma, Wei Peng, Miaomiao Zhang, Ning Li and Jingwen Deng. Their work appears in journals such as Biomedicine & Pharmacotherapy, Cell Biochemistry and Function, Acta Physico-Chimica Sinica, Phytotherapy Research and Journal of Natural Medicines.

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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