John M. Lamar

3.1k total citations · 1 hit paper
38 papers, 2.3k citations indexed

About

John M. Lamar is a scholar working on Molecular Biology, Cell Biology and Immunology and Allergy. According to data from OpenAlex, John M. Lamar has authored 38 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 22 papers in Cell Biology and 16 papers in Immunology and Allergy. Recurrent topics in John M. Lamar's work include Hippo pathway signaling and YAP/TAZ (16 papers), Cell Adhesion Molecules Research (16 papers) and Cellular Mechanics and Interactions (7 papers). John M. Lamar is often cited by papers focused on Hippo pathway signaling and YAP/TAZ (16 papers), Cell Adhesion Molecules Research (16 papers) and Cellular Mechanics and Interactions (7 papers). John M. Lamar collaborates with scholars based in United States, India and Australia. John M. Lamar's co-authors include Richard O. Hynes, Zhigang Jiang, Patrick Stern, Hui Liu, Jeffrey W. Schindler, C. Michael DiPersio, Alexandra Naba, Steven A. Carr, Yuxuan Xiao and Karl R. Clauser and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

John M. Lamar

35 papers receiving 2.3k citations

Hit Papers

The Hippo pathway target, YAP, promotes metastasis throug... 2012 2026 2016 2021 2012 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John M. Lamar United States 21 1.2k 1.1k 649 404 384 38 2.3k
Jill K. Slack‐Davis United States 19 1.1k 0.9× 767 0.7× 580 0.9× 661 1.6× 358 0.9× 29 2.1k
Marta Canel United Kingdom 20 1.0k 0.9× 480 0.4× 590 0.9× 526 1.3× 320 0.8× 27 1.9k
Fernando Calvo Spain 22 1.8k 1.5× 1.1k 1.0× 1.0k 1.6× 155 0.4× 467 1.2× 42 2.9k
Ana Cerezo Spain 13 1.1k 0.9× 677 0.6× 841 1.3× 146 0.4× 358 0.9× 16 2.0k
Kiyoko Yoshioka Japan 23 1.6k 1.3× 626 0.6× 693 1.1× 278 0.7× 339 0.9× 38 2.4k
Jonathan A. Kelber United States 22 1.2k 1.0× 599 0.6× 583 0.9× 116 0.3× 301 0.8× 40 1.9k
Berit B. Tysnes Norway 22 1.1k 1.0× 379 0.4× 655 1.0× 216 0.5× 412 1.1× 29 1.9k
Naira V. Margaryan United States 28 2.1k 1.8× 492 0.5× 1.1k 1.8× 214 0.5× 720 1.9× 57 2.8k
Emma T. Bowden United States 17 1.1k 0.9× 619 0.6× 407 0.6× 330 0.8× 319 0.8× 25 1.8k
Danielle Murphy United States 16 1.5k 1.3× 544 0.5× 544 0.8× 289 0.7× 1.0k 2.7× 23 2.5k

Countries citing papers authored by John M. Lamar

Since Specialization
Citations

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

Fields of papers citing papers by John M. Lamar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John M. Lamar. 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 John M. Lamar. The network helps show where John M. Lamar may publish in the future.

Co-authorship network of co-authors of John M. Lamar

This figure shows the co-authorship network connecting the top 25 collaborators of John M. Lamar. A scholar is included among the top collaborators of John M. Lamar 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 John M. Lamar. John M. Lamar 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.
Purwin, Timothy J., Panyu Chen, Somenath Chowdhury, et al.. (2025). Targeting TAZ-TEAD in minimal residual disease enhances the duration of targeted therapy in melanoma models. Nature Communications. 16(1). 9655–9655.
3.
4.
Lamar, John M., et al.. (2024). Keratinocyte Integrin α3β1 Promotes Efficient Healing of Wound Epidermis. SHILAP Revista de lepidopterología. 5(1). 100310–100310. 3 indexed citations
5.
Stern, Patrick, et al.. (2024). Identification of a Gene Signature That Predicts Dependence upon YAP/TAZ-TEAD. Cancers. 16(5). 852–852. 4 indexed citations
6.
Seavey, Caleb N., et al.. (2023). Loss of CDKN2A Cooperates with WWTR1(TAZ)–CAMTA1 Gene Fusion to Promote Tumor Progression in Epithelioid Hemangioendothelioma. Clinical Cancer Research. 29(13). 2480–2493. 16 indexed citations
7.
Diaz, Miguel F., Megan Livingston, John M. Lamar, et al.. (2022). RhoA‐ROCK competes with YAP to regulate amoeboid breast cancer cell migration in response to lymphatic‐like flow. FASEB BioAdvances. 4(5). 342–361. 14 indexed citations
8.
Seavey, Caleb N., Ajaybabu V. Pobbati, Shuang Ma, et al.. (2021). WWTR1(TAZ)-CAMTA1 gene fusion is sufficient to dysregulate YAP/TAZ signaling and drive epithelioid hemangioendothelioma tumorigenesis. Genes & Development. 35(7-8). 512–527. 45 indexed citations
10.
Miskin, Rakshitha Pandulal, et al.. (2021). Integrin α3β1 Promotes Invasive and Metastatic Properties of Breast Cancer Cells through Induction of the Brn-2 Transcription Factor. Cancers. 13(3). 480–480. 20 indexed citations
11.
Hebert, Jess D., Samuel A. Myers, Alexandra Naba, et al.. (2020). Proteomic Profiling of the ECM of Xenograft Breast Cancer Metastases in Different Organs Reveals Distinct Metastatic Niches. Cancer Research. 80(7). 1475–1485. 90 indexed citations
12.
Ward, Jamie, Alena Rudkouskaya, John M. Lamar, et al.. (2020). Complex Rab4-Mediated Regulation of Endosomal Size and EGFR Activation. Molecular Cancer Research. 18(5). 757–773. 19 indexed citations
13.
Hebert, Jess D., Chenxi Tian, John M. Lamar, et al.. (2020). The scaffold protein IQGAP1 is crucial for extravasation and metastasis. Scientific Reports. 10(1). 2439–2439. 9 indexed citations
14.
Redvers, Richard P., Marit Valla, Anna M. Bofin, et al.. (2018). Nephronectin is Correlated with Poor Prognosis in Breast Cancer and Promotes Metastasis via its Integrin-Binding Motifs. Neoplasia. 20(4). 387–400. 28 indexed citations
15.
Oudin, Madeleine J., Oliver Jonas, Tatsiana Kosciuk, et al.. (2016). Tumor Cell–Driven Extracellular Matrix Remodeling Drives Haptotaxis during Metastatic Progression. Cancer Discovery. 6(5). 516–531. 155 indexed citations
16.
Chen, Michelle B., John M. Lamar, Ran Li, Richard O. Hynes, & Roger D. Kamm. (2016). Elucidation of the Roles of Tumor Integrin β1 in the Extravasation Stage of the Metastasis Cascade. Cancer Research. 76(9). 2513–2524. 133 indexed citations
17.
Reticker-Flynn, Nathan E., Monte M. Winslow, John M. Lamar, et al.. (2012). A combinatorial extracellular matrix platform identifies cell-extracellular matrix interactions that correlate with metastasis. RePEc: Research Papers in Economics. 3 indexed citations
18.
Reticker-Flynn, Nathan E., Monte M. Winslow, John M. Lamar, et al.. (2012). A combinatorial extracellular matrix platform identifies cell-extracellular matrix interactions that correlate with metastasis. Nature Communications. 3(1). 1122–1122. 164 indexed citations
19.
Tavora, Bernardo, Stephen D. Robinson, Louise E. Reynolds, et al.. (2010). Endothelial α3β1-Integrin Represses Pathological Angiogenesis and Sustains Endothelial-VEGF. American Journal Of Pathology. 177(3). 1534–1548. 51 indexed citations
20.
Lamar, John M., Kevin Pumiglia, & C. Michael DiPersio. (2008). An Immortalization-Dependent Switch in Integrin Function Up-regulates MMP-9 to Enhance Tumor Cell Invasion. Cancer Research. 68(18). 7371–7379. 41 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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