John D. Mayfield

810 total citations
18 papers, 434 citations indexed

About

John D. Mayfield is a scholar working on Molecular Biology, Epidemiology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, John D. Mayfield has authored 18 papers receiving a total of 434 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 4 papers in Epidemiology and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in John D. Mayfield's work include Plant Molecular Biology Research (4 papers), Photosynthetic Processes and Mechanisms (3 papers) and 14-3-3 protein interactions (3 papers). John D. Mayfield is often cited by papers focused on Plant Molecular Biology Research (4 papers), Photosynthetic Processes and Mechanisms (3 papers) and 14-3-3 protein interactions (3 papers). John D. Mayfield collaborates with scholars based in United States. John D. Mayfield's co-authors include Anna‐Lisa Paul, Robert J. Ferl, Kevin M. Folta, William E. Gillanders, Timothy P. Fleming, Narendra V. Sankpal, Bradley S. Schoch, Kevin W. Farmer, Aimee M. Struk and Thomas W. Wright and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLANT PHYSIOLOGY and Cancer Research.

In The Last Decade

John D. Mayfield

15 papers receiving 431 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John D. Mayfield United States 8 204 122 107 63 61 18 434
Matthias Geiger Germany 9 137 0.7× 47 0.4× 134 1.3× 101 1.6× 97 1.6× 10 464
Yoko Ino Japan 13 282 1.4× 43 0.4× 87 0.8× 22 0.3× 47 0.8× 37 510
Jai‐Nien Tung Taiwan 11 116 0.6× 15 0.1× 61 0.6× 106 1.7× 81 1.3× 23 360
Aleksandra Nowicka Poland 10 195 1.0× 15 0.1× 102 1.0× 36 0.6× 47 0.8× 25 413
Lidan Xu China 15 234 1.1× 8 0.1× 73 0.7× 39 0.6× 51 0.8× 48 477
Jocelyn S. Kasper United States 10 615 3.0× 33 0.3× 305 2.9× 70 1.1× 59 1.0× 12 843
Magdalena Piątkowska Poland 11 167 0.8× 18 0.1× 54 0.5× 11 0.2× 17 0.3× 34 380
Tiefen Su China 8 314 1.5× 24 0.2× 101 0.9× 15 0.2× 49 0.8× 10 480
Aili Yang China 8 177 0.9× 10 0.1× 144 1.3× 37 0.6× 27 0.4× 22 396
Ana Paula Martins Sebastião Brazil 14 118 0.6× 14 0.1× 133 1.2× 63 1.0× 108 1.8× 43 492

Countries citing papers authored by John D. Mayfield

Since Specialization
Citations

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

Fields of papers citing papers by John D. Mayfield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John D. Mayfield

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

All Works

18 of 18 papers shown
1.
Mayfield, John D., Dana Ataya, Mahmoud A. Abdalah, et al.. (2024). Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI. Radiology Artificial Intelligence. 6(5). e230348–e230348. 1 indexed citations
2.
Mayfield, John D., et al.. (2024). Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model. Journal of Imaging Informatics in Medicine. 38(4). 2063–2070.
3.
Mayfield, John D., et al.. (2024). Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability. Journal of Imaging Informatics in Medicine. 37(6). 3231–3249. 4 indexed citations
4.
Mayfield, John D. & Issam El Naqa. (2024). Evaluation of VQC-LSTM for disability forecasting in multiple sclerosis using sequential multisequence MRI. Quantum Machine Intelligence. 6(2).
5.
Leonard, Russell L., et al.. (2023). Biocompatibility of antifogging SiO-doped Diamond-Like carbon laparoscope coatings. Applied Surface Science. 634. 157606–157606. 2 indexed citations
6.
Mayfield, John D., et al.. (2023). Pilot Lightweight Denoising Algorithm for Multiple Sclerosis on Spine MRI. Journal of Digital Imaging. 36(4). 1877–1884. 1 indexed citations
7.
Mayfield, John D., et al.. (2021). Pulmonary Venous Aneurysm Coiling for Hemoptysis via Known Patent Foramen Ovale. Journal of Vascular and Interventional Radiology. 32(2). 310–311.
8.
Mayfield, John D., et al.. (2020). Peritoneal loose bodies and the differentiation of fatty abdominal and pelvic lesions. SHILAP Revista de lepidopterología. 15(9). 1506–1511. 1 indexed citations
9.
Morris, C.G., et al.. (2019). Challenging the Concept that Late Recurrence and Death from Tumor are Common after Fractionated Radiotherapy for Benign Meningioma. International Journal of Radiation Oncology*Biology*Physics. 105(1). E68–E68. 1 indexed citations
10.
11.
King, Joseph J., John D. Mayfield, Kevin W. Farmer, et al.. (2018). The effect of lower socioeconomic status insurance on outcomes after primary shoulder arthroplasty. Journal of Shoulder and Elbow Surgery. 27(6). S35–S42. 49 indexed citations
12.
Subbiah, Vivek, Caitlin McMahon, Shreyaskumar Patel, et al.. (2015). STUMP un“stumped”: anti-tumor response to anaplastic lymphoma kinase (ALK) inhibitor based targeted therapy in uterine inflammatory myofibroblastic tumor with myxoid features harboring DCTN1-ALK fusion. Journal of Hematology & Oncology. 8(1). 66–66. 73 indexed citations
13.
Mayfield, John D., Anna‐Lisa Paul, & Robert J. Ferl. (2012). The 14-3-3 proteins of Arabidopsis regulate root growth and chloroplast development as components of the photosensory system. Journal of Experimental Botany. 63(8). 3061–3070. 47 indexed citations
14.
Sankpal, Narendra V., et al.. (2011). Activator protein 1 (AP-1) contributes to EpCAM-dependent breast cancer invasion. Breast Cancer Research. 13(6). R124–R124. 39 indexed citations
15.
Paul, Anna‐Lisa, et al.. (2011). Parabolic Flight Induces Changes in Gene Expression Patterns in Arabidopsis thaliana. Astrobiology. 11(8). 743–758. 31 indexed citations
16.
Sankpal, Narendra V., et al.. (2009). Transcriptional Repression of Epithelial Cell Adhesion Molecule Contributes to p53 Control of Breast Cancer Invasion. Cancer Research. 69(3). 753–757. 76 indexed citations
17.
Folta, Kevin M., Anna‐Lisa Paul, John D. Mayfield, & Robert J. Ferl. (2008). 14-3-3 isoforms participate in red light signaling and photoperiodic flowering. Plant Signaling & Behavior. 3(5). 304–306. 15 indexed citations
18.
Mayfield, John D., Kevin M. Folta, Anna‐Lisa Paul, & Robert J. Ferl. (2007). The 14-3-3 Proteins μ and υ Influence Transition to Flowering and Early Phytochrome Response. PLANT PHYSIOLOGY. 145(4). 1692–1702. 89 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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