Mayur Saxena

460 total citations
9 papers, 312 citations indexed

About

Mayur Saxena is a scholar working on Cell Biology, Occupational Therapy and Rehabilitation. According to data from OpenAlex, Mayur Saxena has authored 9 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cell Biology, 2 papers in Occupational Therapy and 2 papers in Rehabilitation. Recurrent topics in Mayur Saxena's work include Cellular Mechanics and Interactions (5 papers), Force Microscopy Techniques and Applications (2 papers) and Artificial Intelligence in Healthcare (2 papers). Mayur Saxena is often cited by papers focused on Cellular Mechanics and Interactions (5 papers), Force Microscopy Techniques and Applications (2 papers) and Artificial Intelligence in Healthcare (2 papers). Mayur Saxena collaborates with scholars based in United States, Singapore and Israel. Mayur Saxena's co-authors include Michael P. Sheetz, James Hone, Haguy Wolfenson, Rishita Changede, Ai Kia Yip, Katsuhiko Iwasaki, Keng‐Hwee Chiam, Yasuhiro Sawada, Ichiro Harada and Hiroaki Machiyama and has published in prestigious journals such as Nature Materials, Nano Letters and Scientific Reports.

In The Last Decade

Mayur Saxena

9 papers receiving 311 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mayur Saxena United States	9	196	100	52	52	37	9	312
Christine Fiddler United Kingdom	7	108 0.6×	120 1.2×	31 0.6×	19 0.4×	25 0.7×	13	316
Toyonobu Yamashita Japan	10	122 0.6×	86 0.9×	19 0.4×	3 0.1×	17 0.5×	23	360
Daniel Flormann Germany	8	81 0.4×	67 0.7×	40 0.8×	6 0.1×	39 1.1×	10	269
Mohammad Soheilypour United States	11	113 0.6×	43 0.4×	203 3.9×	6 0.1×	26 0.7×	13	315
Toru Fukuda Japan	10	29 0.1×	82 0.8×	52 1.0×	6 0.1×	15 0.4×	17	233
T. Kitazawa Japan	5	21 0.1×	15 0.1×	28 0.5×	85 1.6×	15 0.4×	11	341
Haojie Li China	10	20 0.1×	46 0.5×	254 4.9×	6 0.1×	29 0.8×	42	472
Qiyao Li United States	7	24 0.1×	83 0.8×	92 1.8×	3 0.1×	6 0.2×	11	341
Shuichiro FUKUSHIMA Japan	10	33 0.2×	114 1.1×	35 0.7×	4 0.1×	15 0.4×	26	313
Gladys G. Lawrence United States	10	40 0.2×	53 0.5×	132 2.5×	8 0.2×	3 0.1×	14	340

Countries citing papers authored by Mayur Saxena

Since Specialization

Citations

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

Fields of papers citing papers by Mayur Saxena

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mayur Saxena

This figure shows the co-authorship network connecting the top 25 collaborators of Mayur Saxena. A scholar is included among the top collaborators of Mayur Saxena 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 Mayur Saxena. Mayur Saxena is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Yang, Bo, et al.. (2022). Tumor Suppressor DAPK1 Catalyzes Adhesion Assembly on Rigid but Anoikis on Soft Matrices. Frontiers in Cell and Developmental Biology. 10. 959521–959521. 9 indexed citations

2.

Nagamine, Tasha, et al.. (2022). Data-driven identification of heart failure disease states and progression pathways using electronic health records. Scientific Reports. 12(1). 17871–17871. 13 indexed citations

3.

Liu, Helen H., Mayur Saxena, Michael A. Castellano, et al.. (2021). Development of a Method for Clinical Evaluation of Artificial Intelligence–Based Digital Wound Assessment Tools. JAMA Network Open. 4(5). e217234–e217234. 33 indexed citations

4.

Nagamine, Tasha, et al.. (2020). Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data. Scientific Reports. 10(1). 21340–21340. 24 indexed citations

5.

Rupprecht, Jean-François, Mayur Saxena, James Hone, et al.. (2019). Large and reversible myosin-dependent forces in rigidity sensing. Nature Physics. 15(7). 689–695. 24 indexed citations

6.

Saxena, Mayur, et al.. (2018). The Future of Data‐Driven Wound Care. AORN Journal. 107(4). 455–463. 8 indexed citations

7.

Saxena, Mayur, Shuaimin Liu, Bo Yang, et al.. (2017). EGFR and HER2 activate rigidity sensing only on rigid matrices. Nature Materials. 16(7). 775–781. 59 indexed citations

8.

Saxena, Mayur, Rishita Changede, James Hone, Haguy Wolfenson, & Michael P. Sheetz. (2017). Force-Induced Calpain Cleavage of Talin Is Critical for Growth, Adhesion Development, and Rigidity Sensing. Nano Letters. 17(12). 7242–7251. 43 indexed citations

9.

Yip, Ai Kia, Katsuhiko Iwasaki, Hiroaki Machiyama, et al.. (2013). Cellular Response to Substrate Rigidity Is Governed by Either Stress or Strain. Biophysical Journal. 104(1). 19–29. 99 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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