John D. Bukowy

745 total citations
21 papers, 528 citations indexed

About

John D. Bukowy is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, John D. Bukowy has authored 21 papers receiving a total of 528 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Molecular Biology and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in John D. Bukowy's work include Radiomics and Machine Learning in Medical Imaging (5 papers), Prostate Cancer Diagnosis and Treatment (5 papers) and AI in cancer detection (4 papers). John D. Bukowy is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), Prostate Cancer Diagnosis and Treatment (5 papers) and AI in cancer detection (4 papers). John D. Bukowy collaborates with scholars based in United States, Switzerland and Netherlands. John D. Bukowy's co-authors include Allen W. Cowley, Theresa Kurth, Alex Dayton, Louise Evans, Alexander Staruschenko, Chun Yang, Vikash Kumar, Andrew S. Greene, Peter S. LaViolette and Daniel Beard and has published in prestigious journals such as PLoS ONE, The FASEB Journal and Journal of Applied Physiology.

In The Last Decade

John D. Bukowy

21 papers receiving 527 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. Bukowy United States 12 127 118 117 89 82 21 528
Bomi Kim South Korea 16 76 0.6× 143 1.2× 82 0.7× 46 0.5× 22 0.3× 52 678
Yunzhi Wang China 11 108 0.9× 71 0.6× 43 0.4× 45 0.5× 38 0.5× 29 350
Akinori Higaki Japan 13 41 0.3× 83 0.7× 33 0.3× 22 0.2× 126 1.5× 52 476
Andrew Wu United States 12 64 0.5× 192 1.6× 78 0.7× 26 0.3× 149 1.8× 34 557
Sergio Solorio Mexico 13 101 0.8× 51 0.4× 75 0.6× 13 0.1× 90 1.1× 59 485
Rong Shi China 11 142 1.1× 191 1.6× 53 0.5× 26 0.3× 212 2.6× 34 672
Casper Nielsen United Kingdom 10 107 0.8× 110 0.9× 74 0.6× 17 0.2× 29 0.4× 17 724
Hongjie Wang China 15 24 0.2× 146 1.2× 52 0.4× 17 0.2× 140 1.7× 31 823
Julie A. Douglas United States 17 30 0.2× 409 3.5× 132 1.1× 33 0.4× 38 0.5× 35 1.0k
Kōji Abe Japan 14 97 0.8× 96 0.8× 54 0.5× 14 0.2× 244 3.0× 47 554

Countries citing papers authored by John D. Bukowy

Since Specialization
Citations

This map shows the geographic impact of John D. Bukowy'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. Bukowy 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. Bukowy more than expected).

Fields of papers citing papers by John D. Bukowy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

This figure shows the co-authorship network connecting the top 25 collaborators of John D. Bukowy. A scholar is included among the top collaborators of John D. Bukowy 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. Bukowy. John D. Bukowy 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.
Bobholz, Samuel, Allison Lowman, Jennifer Connelly, et al.. (2024). Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement. Neurosurgery. 95(3). 537–547. 4 indexed citations
2.
Tyshynsky, Roman, Maureen Riedl, John D. Bukowy, et al.. (2023). Periglomerular afferent innervation of the mouse renal cortex. Frontiers in Neuroscience. 17. 974197–974197. 14 indexed citations
3.
Bobholz, Samuel, Michael Barrett, Allison Lowman, et al.. (2023). T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers. 15(18). 4437–4437. 7 indexed citations
4.
Lowman, Allison, et al.. (2022). Homologous point transformer for multi-modality prostate image registration. PeerJ Computer Science. 8. e1155–e1155. 2 indexed citations
5.
Dasinger, John Henry, Justine M. Abais‐Battad, John D. Bukowy, et al.. (2021). Dietary protein source contributes to the risk of developing maternal syndrome in the Dahl salt-sensitive rat. Pregnancy Hypertension. 24. 126–134. 5 indexed citations
6.
Bukowy, John D., Sean D. McGarry, Allison Lowman, et al.. (2020). Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network. Journal of Medical Imaging. 7(5). 57501–57501. 9 indexed citations
7.
McGarry, Sean D., John D. Bukowy, Kenneth A. Iczkowski, et al.. (2020). Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer. Journal of Medical Imaging. 7(5). 54501–54501. 14 indexed citations
8.
Palygin, Oleg, Denisha Spires, Vladislav Levchenko, et al.. (2019). Progression of diabetic kidney disease in T2DN rats. American Journal of Physiology-Renal Physiology. 317(6). F1450–F1461. 46 indexed citations
9.
McDermott‐Roe, Chris, Wenjian Lv, Shogo Wada, et al.. (2019). Investigation of a dilated cardiomyopathy–associated variant in BAG3 using genome-edited iPSC-derived cardiomyocytes. JCI Insight. 4(22). 34 indexed citations
10.
McGarry, Sean D., John D. Bukowy, Kenneth A. Iczkowski, et al.. (2019). Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space. Tomography. 5(1). 127–134. 41 indexed citations
11.
Bukowy, John D., Sean D. McGarry, Andrew S. Nencka, et al.. (2019). Classification before Segmentation: Improved U-Net Prostate Segmentation. 1–4. 8 indexed citations
12.
McGarry, Sean D., Kenneth A. Iczkowski, William A. Hall, et al.. (2018). Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer. International Journal of Radiation Oncology*Biology*Physics. 101(5). 1179–1187. 43 indexed citations
13.
Kumar, Vikash, Louise Evans, Theresa Kurth, et al.. (2018). Therapeutic Suppression of mTOR (Mammalian Target of Rapamycin) Signaling Prevents and Reverses Salt-Induced Hypertension and Kidney Injury in Dahl Salt-Sensitive Rats. Hypertension. 73(3). 630–639. 36 indexed citations
14.
Bukowy, John D., Alex Dayton, Alexander Staruschenko, et al.. (2018). Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections. Journal of the American Society of Nephrology. 29(8). 2081–2088. 84 indexed citations
15.
McDermott‐Roe, Chris, Marion Leleu, Glenn C. Rowe, et al.. (2017). Transcriptome-wide co-expression analysis identifies LRRC2 as a novel mediator of mitochondrial and cardiac function. PLoS ONE. 12(2). e0170458–e0170458. 9 indexed citations
16.
17.
Evans, Louise, Theresa Kurth, Chun Yang, et al.. (2017). Increased Perfusion Pressure Drives Renal T-Cell Infiltration in the Dahl Salt-Sensitive Rat. Hypertension. 70(3). 543–551. 61 indexed citations
18.
Dayton, Alex, John D. Bukowy, Timothy J. Stodola, et al.. (2016). Breaking the Cycle. Hypertension. 68(5). 1139–1144. 47 indexed citations
19.
Prisco, Anthony, et al.. (2014). Automated Quantification Reveals Hyperglycemia Inhibits Endothelial Angiogenic Function. PLoS ONE. 9(4). e94599–e94599. 16 indexed citations
20.
Miller, Justin, et al.. (2014). Changes in glutamate receptor subunits within the medulla in goats after section of the carotid sinus nerves. Journal of Applied Physiology. 116(12). 1531–1542. 8 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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