Bin Wu

3.2k citations
127 papers · 2.4k · 1 hit paper · h-index 28

Impact in

Papers in

Bin Wu

120 papers receiving 2.4k citations

Bin Wu's Hit Papers

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS 2025 · 26 citations
260Years since publication510152025

Peers

Bin Wu
Comparison fields: 5 of 150
  • Aging 60
  • Biological Psychiatry 50
  • Geriatrics and Gerontology 68
  • Cancer Research 290
  • Pharmacology 134
Replace Shijin Xia with:
Shijin Xia China
George W. Booz United States
Byeong Hwa Jeon South Korea
Wei Hu China
Fan Fan United States
Eiji Warabi Japan
Junling Gao China
Fu‐Ming Shen China
Bin Wu relative to Shijin Xia China Shijin Xia's profile →
Citations per field
00.5×1.5×
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Citations per year

Countries citing papers authored by Bin Wu

Since Specialization
Citations

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

Fields of papers citing papers by Bin Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 127 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010111
2 2011104
3 202088
4 201183
5 201564
6 201463
7 201762
8 201558
9 202258
10 200955
11 201153
12 200853
13 201548
14 202045
15 202245
16 200843
17 201743
18 201642
19 201641
20 201539

About Bin Wu

Bin Wu is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine, Physiology, Cancer Research and Oncology, having authored 127 papers that have together received 2.4k indexed citations. Recurring topics across this work include Chronic Obstructive Pulmonary Disease (COPD) Research (10 papers), Pain Mechanisms and Treatments (7 papers), Ion channel regulation and function (6 papers), Metabolomics and Mass Spectrometry Studies (6 papers), Cancer-related molecular mechanisms research (4 papers), Pharmacological Effects of Natural Compounds (4 papers), MicroRNA in disease regulation (4 papers) and Circular RNAs in diseases (4 papers). The work is most often cited by research in Aging (60 citations), Biological Psychiatry (50 citations), Geriatrics and Gerontology (68 citations), Cancer Research (290 citations) and Pharmacology (134 citations). Bin Wu has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Dongming Li, Tianwen Lai, Xuanna Zhao, Jianhua Huang, Zhixiong Yang, Dong Wu, Weiyi Fang, Huiling Yang, Weiren Luo and Lixia Li. Their work appears in journals such as International Journal of COPD, Scientific Reports, Frontiers in Pharmacology, Medicine and Bioorganic Chemistry.

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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