Pei-Hsun Wu

5.2k total citations
61 papers, 2.9k citations indexed

About

Pei-Hsun Wu is a scholar working on Cell Biology, Molecular Biology and Oncology. According to data from OpenAlex, Pei-Hsun Wu has authored 61 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Cell Biology, 22 papers in Molecular Biology and 15 papers in Oncology. Recurrent topics in Pei-Hsun Wu's work include Cellular Mechanics and Interactions (19 papers), Cell Image Analysis Techniques (12 papers) and Cancer Cells and Metastasis (10 papers). Pei-Hsun Wu is often cited by papers focused on Cellular Mechanics and Interactions (19 papers), Cell Image Analysis Techniques (12 papers) and Cancer Cells and Metastasis (10 papers). Pei-Hsun Wu collaborates with scholars based in United States, China and South Korea. Pei-Hsun Wu's co-authors include Denis Wirtz, Anjil Giri, Sean X. Sun, Daniele M. Gilkes, Jude M. Phillip, Ethan G. Hughes, Gregory D. Longmore, Amit Agarwal, Dwight E. Bergles and Max A. Tischfield and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Angewandte Chemie International Edition and Nature Communications.

In The Last Decade

Pei-Hsun Wu

59 papers receiving 2.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pei-Hsun Wu United States 27 1.2k 939 780 556 308 61 2.9k
Ghassan Mouneimne United States 25 1.3k 1.1× 1.4k 1.5× 673 0.9× 731 1.3× 223 0.7× 44 3.2k
Matthew W. Conklin United States 25 1.1k 0.9× 880 0.9× 791 1.0× 1.1k 2.0× 338 1.1× 37 3.1k
Guillaume Jacquemet Finland 31 1.6k 1.4× 1.5k 1.6× 494 0.6× 431 0.8× 454 1.5× 59 3.7k
Emad Moeendarbary United Kingdom 31 1.4k 1.1× 2.3k 2.5× 1.3k 1.7× 889 1.6× 220 0.7× 73 4.5k
Susana Rocha Belgium 29 1.8k 1.5× 514 0.5× 632 0.8× 228 0.4× 499 1.6× 97 3.4k
Hellyeh Hamidi Finland 19 1.4k 1.2× 1.0k 1.1× 416 0.5× 647 1.2× 163 0.5× 24 3.0k
Alexander R. Dunn United States 38 1.9k 1.6× 2.4k 2.6× 977 1.3× 741 1.3× 272 0.9× 106 4.9k
Mingxing Ouyang United States 20 912 0.8× 1.1k 1.2× 488 0.6× 219 0.4× 241 0.8× 44 2.1k
Alba Diz-Muñoz Germany 22 987 0.8× 1.5k 1.6× 682 0.9× 250 0.4× 204 0.7× 35 2.6k
Sophie A. Lelièvre United States 31 1.8k 1.5× 772 0.8× 812 1.0× 1.4k 2.4× 268 0.9× 73 3.6k

Countries citing papers authored by Pei-Hsun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Pei-Hsun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pei-Hsun Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Pei-Hsun Wu. A scholar is included among the top collaborators of Pei-Hsun Wu 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 Pei-Hsun Wu. Pei-Hsun Wu 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
1.
Macaluso, Nicolas C., Yukang Li, Bartholomew Starich, et al.. (2025). Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts. Science Advances. 11(17). eads1875–eads1875. 3 indexed citations
2.
Wu, Pei-Hsun, et al.. (2025). Methods to analyze cell migration data: fundamentals and practical guidelines. Nature Methods. 23(1). 43–55. 1 indexed citations
3.
Nair, Praful R., et al.. (2025). Selecting the optimal cell migration assay: fundamentals and practical guidelines. Nature Methods. 23(1). 30–42. 1 indexed citations
4.
Liu, Kuan-Ting, Tingyu Lu, Yuting Lin, et al.. (2024). Extracellular vesicles from human adipose-derived stem cell spheroids: Characterization and therapeutic implications in diabetic wound healing. Materials Today Bio. 29. 101333–101333. 3 indexed citations
5.
Zhang, Huilin, et al.. (2024). Perceived professional preparedness and identity among senior nursing students: a latent profile Analysis. BMC Nursing. 23(1). 7 indexed citations
6.
Bons, Joanna, Meng‐Horng Lee, Pei-Hsun Wu, et al.. (2024). Combined assembloid modeling and 3D whole-organ mapping captures the microanatomy and function of the human fallopian tube. Science Advances. 10(39). eadp6285–eadp6285. 5 indexed citations
7.
Xue, Yingchao, Ashley Kiemen, Dominic Henn, et al.. (2022). Mechanical tension mobilizes Lgr6 + epidermal stem cells to drive skin growth. Science Advances. 8(17). eabl8698–eabl8698. 22 indexed citations
8.
Sneider, Alexandra, Ashley Kiemen, Joo Ho Kim, et al.. (2022). Deep learning identification of stiffness markers in breast cancer. Biomaterials. 285. 121540–121540. 15 indexed citations
9.
Phillip, Jude M., Nahuel Zamponi, Wadsworth A. Williams, et al.. (2021). Fractional re-distribution among cell motility states during ageing. Communications Biology. 4(1). 81–81. 10 indexed citations
10.
Phillip, Jude M., Kyu Sang Han, Wei-Chiang Chen, Denis Wirtz, & Pei-Hsun Wu. (2021). A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Nature Protocols. 16(2). 754–774. 66 indexed citations
11.
Wu, Pei-Hsun, Daniele M. Gilkes, Jude M. Phillip, et al.. (2020). Single-cell morphology encodes metastatic potential. Science Advances. 6(4). eaaw6938–eaaw6938. 124 indexed citations
12.
Ardeljan, Daniel, Jared P. Steranka, Chunhong Liu, et al.. (2020). Cell fitness screens reveal a conflict between LINE-1 retrotransposition and DNA replication. Nature Structural & Molecular Biology. 27(2). 168–178. 80 indexed citations
13.
Duan, Xing, Yizeng Li, Kexi Yi, et al.. (2020). Dynamic organelle distribution initiates actin-based spindle migration in mouse oocytes. Nature Communications. 11(1). 277–277. 58 indexed citations
14.
Wu, Pei-Hsun, Daniele M. Gilkes, & Denis Wirtz. (2018). The Biophysics of 3D Cell Migration. Annual Review of Biophysics. 47(1). 549–567. 33 indexed citations
15.
Noë, Michaël, Neda Rezaee, Kaushal Asrani, et al.. (2018). Immunolabeling of Cleared Human Pancreata Provides Insights into Three-Dimensional Pancreatic Anatomy and Pathology. American Journal Of Pathology. 188(7). 1530–1535. 33 indexed citations
16.
Harris, Michael J., Denis Wirtz, & Pei-Hsun Wu. (2018). Dissecting cellular mechanics: Implications for aging, cancer, and immunity. Seminars in Cell and Developmental Biology. 93. 16–25. 23 indexed citations
17.
Ju, Julia A., Inês Godet, Hasini Jayatilaka, et al.. (2017). Hypoxia Selectively Enhances Integrin α5β1 Receptor Expression in Breast Cancer to Promote Metastasis. Molecular Cancer Research. 15(6). 723–734. 101 indexed citations
18.
Wu, Pei-Hsun, Anjil Giri, Sean X. Sun, & Denis Wirtz. (2014). Three-dimensional cell migration does not follow a random walk. Proceedings of the National Academy of Sciences. 111(11). 3949–3954. 237 indexed citations
19.
Wu, Pei-Hsun, et al.. (2012). High-throughput ballistic injection nanorheology to measure cell mechanics. Nature Protocols. 7(1). 155–170. 44 indexed citations
20.
Wu, Pei-Hsun, Ashutosh Agarwal, Henry Hess, Pramod P. Khargonekar, & Yiider Tseng. (2010). Analysis of Video-Based Microscopic Particle Trajectories Using Kalman Filtering. Biophysical Journal. 98(12). 2822–2830. 14 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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