Lan He

1.1k total citations · 1 hit paper
30 papers, 766 citations indexed

About

Lan He is a scholar working on Molecular Biology, Physiology and Immunology. According to data from OpenAlex, Lan He has authored 30 papers receiving a total of 766 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 7 papers in Physiology and 6 papers in Immunology. Recurrent topics in Lan He's work include Pain Mechanisms and Treatments (4 papers), Lysosomal Storage Disorders Research (3 papers) and Natural product bioactivities and synthesis (3 papers). Lan He is often cited by papers focused on Pain Mechanisms and Treatments (4 papers), Lysosomal Storage Disorders Research (3 papers) and Natural product bioactivities and synthesis (3 papers). Lan He collaborates with scholars based in China, United States and Germany. Lan He's co-authors include Bing Sun, Claudia Sommer, Yuan Zhang, Wangpeng Gu, Yaguang Zhang, Guomei Lin, Haiyan Zhou, Yanming Wang, Zhengfan Jiang and Shanshan Yan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Immunology and PLoS ONE.

In The Last Decade

Lan He

29 papers receiving 760 citations

Hit Papers

Artificial intelligence in agriculture: Advancing crop pr... 2025 2026 2025 5 10 15 20
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Artificial intelligence in agriculture: Advancing crop productivity and sustainability
    2025 22 citations Lan He, Muhammad Yaqub et al. Journal of Agriculture and Food Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lan He China 13 252 241 216 113 93 30 766
Fengna Chu China 13 255 1.0× 232 1.0× 366 1.7× 92 0.8× 42 0.5× 28 959
Emily Tjon United States 9 107 0.4× 290 1.2× 447 2.1× 54 0.5× 78 0.8× 11 872
Carmen Espejo Spain 28 147 0.6× 510 2.1× 607 2.8× 149 1.3× 91 1.0× 63 1.6k
Wen Huang China 16 134 0.5× 74 0.3× 320 1.5× 85 0.8× 44 0.5× 36 725
Diego Morales‐Scheihing United States 15 386 1.5× 79 0.3× 533 2.5× 149 1.3× 55 0.6× 22 1.0k
Mohammad G. Mohammad United Arab Emirates 15 218 0.9× 241 1.0× 202 0.9× 35 0.3× 38 0.4× 33 804
Carl Weidinger Germany 22 128 0.5× 306 1.3× 543 2.5× 31 0.3× 74 0.8× 40 1.2k
M Wender Poland 15 243 1.0× 171 0.7× 300 1.4× 195 1.7× 49 0.5× 195 1.1k

Countries citing papers authored by Lan He

Since Specialization
Citations

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

Fields of papers citing papers by Lan He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lan He

This figure shows the co-authorship network connecting the top 25 collaborators of Lan He. A scholar is included among the top collaborators of Lan He based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lan He. Lan He is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Yan, Long, et al.. (2025). A hybrid deep learning model for mammographic breast cancer detection: Multi-autoencoder and attention mechanisms. Journal of Radiation Research and Applied Sciences. 18(3). 101578–101578. 1 indexed citations
3.
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He, Lan, et al.. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research. 20. 101762–101762. 22 indexed citations breakdown →
6.
He, Lan, et al.. (2024). Evodiamine inhibits proliferation and induces apoptosis of nasopharyngeal carcinoma cells via the SRC/ERBB2-mediated MAPK/ERK signaling pathway. Journal of Translational Medicine. 22(1). 859–859. 2 indexed citations
7.
Yaqub, Muhammad, et al.. (2024). Intelligent breast cancer diagnosis with two-stage using mammogram images. Scientific Reports. 14(1). 16672–16672. 7 indexed citations
8.
Wang, Wen, Liu Liu, Ting Lin, et al.. (2023). Unraveling the mechanism of Yiqi Jiedu formula against nasopharyngeal carcinoma: An investigation integrating network pharmacology, serum pharmacochemistry, and metabolomics. Journal of Ethnopharmacology. 319(Pt 3). 117343–117343. 7 indexed citations
9.
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Cai, Yufeng, et al.. (2023). Identification of a CircRNA-miRNA-mRNA Network and Integrated Analysis of Immune Infiltration in Oral Squamous Cell Carcinoma. Journal of Cancer. 14(2). 250–261. 1 indexed citations
11.
Zhou, Fangliang, et al.. (2022). Luteolin Isolated from Polygonum cuspidatum Is a Potential Compound against Nasopharyngeal Carcinoma. BioMed Research International. 2022(1). 9740066–9740066. 9 indexed citations
12.
He, Lan, et al.. (2021). [CD44 regulates biological behavior and Ras signaling pathway in nasopharyngeal carcinoma stem cells].. PubMed. 43(2). 180–187. 1 indexed citations
13.
He, Lan, et al.. (2020). Isoimperatorin Induces Apoptosis of Nasopharyngeal Carcinoma Cells via the MAPK/ERK1/2 Signaling Pathway. Evidence-based Complementary and Alternative Medicine. 2020(1). 2138186–2138186. 10 indexed citations
14.
Su, Pan, Sheng Chen, Haiyan Zhou, et al.. (2016). Novel Function of Extracellular Matrix Protein 1 in Suppressing Th17 Cell Development in Experimental Autoimmune Encephalomyelitis. The Journal of Immunology. 197(4). 1054–1064. 22 indexed citations
15.
Xu, Yanhui, Ziru Li, Yue Yin, et al.. (2015). Ghrelin Inhibits the Differentiation of T Helper 17 Cells through mTOR/STAT3 Signaling Pathway. PLoS ONE. 10(2). e0117081–e0117081. 35 indexed citations
16.
Zhang, Yuan, Yaguang Zhang, Wangpeng Gu, Lan He, & Bing Sun. (2014). Th1/Th2 Cell’s Function in Immune System. Advances in experimental medicine and biology. 841. 45–65. 95 indexed citations
17.
Üçeyler, Nurcan, Lan He, Karlheinz Reiners, et al.. (2011). Small fibers in Fabry disease: baseline and follow-up data under enzyme replacement therapy. Journal of the Peripheral Nervous System. 16(4). 304–314. 73 indexed citations
18.
Møller, Anette Torvin, Flemming W. Bach, Ulla Feldt‐Rasmussen, et al.. (2009). Functional and structural nerve fiber findings in heterozygote patients with Fabry disease. Pain. 145(1). 237–245. 63 indexed citations
19.
Krämer, Heidrun H., Lan He, Bao Lu, Frank Birklein, & Claudia Sommer. (2008). Increased pain and neurogenic inflammation in mice deficient of neutral endopeptidase. Neurobiology of Disease. 35(2). 177–183. 29 indexed citations
20.
Obermann, Mark, Zaza Katsarava, Stefan Eßer, et al.. (2007). Correlation of epidermal nerve fiber density with pain-related evoked potentials in HIV neuropathy. Pain. 138(1). 79–86. 64 indexed citations

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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