John M. Millholland

3.6k total citations · 2 hit papers
15 papers, 2.6k citations indexed

About

John M. Millholland is a scholar working on Molecular Biology, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, John M. Millholland has authored 15 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 4 papers in Oncology and 3 papers in Pulmonary and Respiratory Medicine. Recurrent topics in John M. Millholland's work include Cancer Immunotherapy and Biomarkers (3 papers), Iron Metabolism and Disorders (3 papers) and RNA regulation and disease (2 papers). John M. Millholland is often cited by papers focused on Cancer Immunotherapy and Biomarkers (3 papers), Iron Metabolism and Disorders (3 papers) and RNA regulation and disease (2 papers). John M. Millholland collaborates with scholars based in United States, Switzerland and Canada. John M. Millholland's co-authors include Timothy D. Connors, Terence J. Van Raay, Timothy C. Burn, Gregory M. Landes, Jeffery A. Towbin, Arthur J. Moss, K Timothy, G. Michael Vincent, Mark Curran and Igor Splawski and has published in prestigious journals such as Journal of Biological Chemistry, Nature Genetics and The Journal of Experimental Medicine.

In The Last Decade

John M. Millholland

15 papers receiving 2.6k citations

Hit Papers

Positional cloning of a n... 1996 2026 2006 2016 1996 2006 400 800 1.2k

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. Millholland United States 9 2.1k 1.1k 348 326 259 15 2.6k
Yan‐Shan Dai United States 20 2.0k 1.0× 838 0.8× 167 0.5× 271 0.8× 257 1.0× 37 2.6k
Gregory M. Landes United States 26 2.5k 1.2× 1.2k 1.0× 442 1.3× 1.0k 3.2× 162 0.6× 35 3.3k
Anne Pizard France 25 1.2k 0.6× 975 0.9× 145 0.4× 132 0.4× 111 0.4× 39 2.3k
B. Paul Herring United States 32 1.9k 0.9× 612 0.6× 159 0.5× 179 0.5× 128 0.5× 63 2.6k
Florence Botteri Switzerland 17 836 0.4× 254 0.2× 488 1.4× 302 0.9× 160 0.6× 26 2.0k
Maria Nesterova United States 30 1.5k 0.7× 462 0.4× 100 0.3× 214 0.7× 669 2.6× 71 2.8k
Sathivel Ponniah Singapore 16 1.7k 0.8× 847 0.8× 145 0.4× 172 0.5× 147 0.6× 18 2.6k
Christian Wallasch Germany 11 2.3k 1.1× 195 0.2× 473 1.4× 195 0.6× 1.1k 4.2× 14 3.5k
David S. Taylor United Kingdom 26 877 0.4× 300 0.3× 116 0.3× 279 0.9× 104 0.4× 58 1.9k
Steven E. Scherer United States 19 906 0.4× 303 0.3× 83 0.2× 466 1.4× 247 1.0× 37 1.9k

Countries citing papers authored by John M. Millholland

Since Specialization
Citations

This map shows the geographic impact of John M. Millholland'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. Millholland 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. Millholland more than expected).

Fields of papers citing papers by John M. Millholland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

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

All Works

15 of 15 papers shown
1.
Torres, Lidiane S., Lucas Fernando Sérgio Gushiken, Flávia C. Leonardo, et al.. (2024). Targeting P-selectin and interleukin-1β in mice with sickle cell disease: effects on vaso-occlusion, liver injury and organ iron deposition. Haematologica. 110(3). 725–738. 1 indexed citations
2.
Hochberg, Natasha S., Srinivasa P. S. Rao, Gerhild Angyalosi, et al.. (2023). An end is in sight: a perspective on PCR as an endpoint for Chagas disease treatment trials. SHILAP Revista de lepidopterología. 2. 1272386–1272386. 2 indexed citations
3.
Hartl, Dominik, Valéria De Luca, Anna Kostikova, et al.. (2021). Translational precision medicine: an industry perspective. Journal of Translational Medicine. 19(1). 245–245. 78 indexed citations
4.
Jayaraman, Pushpa, Vanessa Rodrik-Outmezguine, John M. Millholland, et al.. (2020). Abstract 5640: Targeting tumor-promoting inflammation (TPI) via the IL-1βpathway for cancer immunotherapy. Cancer Research. 80(16_Supplement). 5640–5640. 3 indexed citations
5.
Jayaraman, Pushpa, John M. Millholland, Neil A. O’Brien, et al.. (2019). Abstract C103: Targeting IL-1β pathway for cancer immunotherapy. Molecular Cancer Therapeutics. 18(12_Supplement). C103–C103. 3 indexed citations
7.
Millholland, John M., Shuqiang Li, Cecilia A. Fernández, & Anthony P. Shuber. (2012). Detection of low-frequency FGFR3 mutations in the urine of patients with bladder cancer using next-generation deep sequencing.. Journal of Clinical Oncology. 30(30_suppl). 59–59. 2 indexed citations
8.
Itzkowitz, Steven H., Randall E. Brand, Lina Jandorf, et al.. (2008). A Simplified, Noninvasive Stool DNA Test for Colorectal Cancer Detection. The American Journal of Gastroenterology. 103(11). 2862–2870. 118 indexed citations
9.
Maillard, Ivan, Lili Tu, Arivazhagan Sambandam, et al.. (2006). The requirement for Notch signaling at the β-selection checkpoint in vivo is absolute and independent of the pre–T cell receptor. The Journal of Experimental Medicine. 203(10). 2239–2245. 162 indexed citations
10.
Weng, Andrew P., John M. Millholland, Yumi Yashiro–Ohtani, et al.. (2006). c-Myc is an important direct target of Notch1 in T-cell acute lymphoblastic leukemia/lymphoma. Genes & Development. 20(15). 2096–2109. 673 indexed citations breakdown →
11.
Linsenmayer, Thomas F., et al.. (2004). Nuclear ferritin in corneal epithelial cells: tissue–specific nuclear transport and protection from UV–damage. Progress in Retinal and Eye Research. 24(2). 139–159. 31 indexed citations
12.
Millholland, John M., et al.. (2003). Ferritoid, a Tissue-specific Nuclear Transport Protein for Ferritin in Corneal Epithelial Cells. Journal of Biological Chemistry. 278(26). 23963–23970. 27 indexed citations
13.
Curran, Mark, Igor Splawski, Timothy C. Burn, et al.. (1996). Positional cloning of a novel potassium channel gene: KVLQT1 mutations cause cardiac arrhythmias. Nature Genetics. 12(1). 17–23. 1321 indexed citations breakdown →
14.
Burn, Timothy C., Timothy D. Connors, Terence J. Van Raay, et al.. (1996). Generation of a transcriptional map for a 700-kb region surrounding the polycystic kidney disease type 1 (PKD1) and tuberous sclerosis type 2 (TSC2) disease genes on human chromosome 16p3.3.. Genome Research. 6(6). 525–537. 20 indexed citations
15.
Burn, Timothy C., Timothy D. Connors, William R. Dackowski, et al.. (1995). Analysis of the genomic sequence for the autosomal dominant polycystic kidney disease (PKD1) gene predicts the presence of a leucine-rich repeat. Human Molecular Genetics. 4(4). 575–582. 199 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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