Gang Lan

606 citations
17 papers · 450 · h-index 12

Impact in

    • MicroRNA in disease regulation
    • Cancer-related molecular mechanisms research
    • Cancer, Lipids, and Metabolism
  • Biochemistry top 10%
    • Sulfur Compounds in Biology

Papers in

    • Cholesterol and Lipid Metabolism 4
    • Peroxisome Proliferator-Activated Receptors 1
    • Glycosylation and Glycoproteins Research 1

Gang Lan

16 papers receiving 444 citations

Peers

Gang Lan
Comparison fields: 5 of 74
  • Cancer Research 184
  • Biochemistry 46
  • Immunology 89
  • Molecular Biology 194
  • Surgery 105
Replace Koko Asakura with:
Koko Asakura Japan
Enze Zheng China
Ju-Qiong Wang China
Max Walker United States
Anke Loregger Netherlands
Vicenta Llorente‐Cortés Spain
Pin Qian China
Tyler J. Marquart United States
Byunggil Yoo United States
Siwei Xia China
Gang Lan relative to Koko Asakura Japan Koko Asakura's profile →
Citations per field
00.5×3.2×
Koko Asakura · 1×
Citations per year

Countries citing papers authored by Gang Lan

Since Specialization
Citations

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

Fields of papers citing papers by Gang Lan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 201668
2 201667
3 201644
4 201541
5 201540
6 201539
7 201636
8 201534
9 200824
10 202014
11 202311
12 202011
13 202210
14 20235
15 20234
16 20242
17 20240

About Gang Lan

Gang Lan is a scholar working on Surgery, Molecular Biology, Immunology, Cancer Research and Pulmonary and Respiratory Medicine, having authored 17 papers that have together received 450 indexed citations. Recurring topics across this work include Cholesterol and Lipid Metabolism (4 papers), Cancer, Lipids, and Metabolism (3 papers), Inflammatory Biomarkers in Disease Prognosis (2 papers), Atherosclerosis and Cardiovascular Diseases (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), Sulfur Compounds in Biology (2 papers), Peroxisome Proliferator-Activated Receptors (1 paper) and Glycosylation and Glycoproteins Research (1 paper). The work is most often cited by research in Cancer Research (184 citations), Biochemistry (46 citations), Immunology (89 citations), Molecular Biology (194 citations) and Surgery (105 citations). Gang Lan has collaborated with scholars based in China and Canada. Frequent co-authors include Xi‐Long Zheng, Chao‐Ke Tang, Wei Xie, Haipeng Cheng, Duo Gong, Feng Yao, Chong Huang, Zhen-Wang Zhao, Liang Li and Guiwu Wei. Their work appears in journals such as Biochemical and Biophysical Research Communications, International Journal of Molecular Sciences, PLoS ONE, Atherosclerosis and Medicine.

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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