Jan Pinkas

3.3k total citations
71 papers, 2.3k citations indexed

About

Jan Pinkas is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Jan Pinkas has authored 71 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Oncology, 37 papers in Radiology, Nuclear Medicine and Imaging and 30 papers in Molecular Biology. Recurrent topics in Jan Pinkas's work include Monoclonal and Polyclonal Antibodies Research (36 papers), HER2/EGFR in Cancer Research (33 papers) and Cancer-related Molecular Pathways (8 papers). Jan Pinkas is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (36 papers), HER2/EGFR in Cancer Research (33 papers) and Cancer-related Molecular Pathways (8 papers). Jan Pinkas collaborates with scholars based in United States, France and Italy. Jan Pinkas's co-authors include Philip Leder, Jon C. Jones, Ryan Erwert, Dan Branstetter, Robert E. Miller, Eva González‐Suárez, Allison P. Jacob, William C. Dougall, Olga Ab and Charlene A. Audette and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Blood.

In The Last Decade

Jan Pinkas

68 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan Pinkas United States 23 1.5k 940 783 275 252 71 2.3k
Maarten L. Janmaat Netherlands 17 1.3k 0.8× 1.0k 1.1× 726 0.9× 567 2.1× 156 0.6× 28 2.4k
Erwin R. Boghaert United States 26 1.0k 0.7× 1.2k 1.3× 513 0.7× 245 0.9× 196 0.8× 58 2.5k
Teresa C. Dugger United States 16 1.7k 1.1× 1.7k 1.8× 388 0.5× 270 1.0× 503 2.0× 22 2.7k
Tiffany K. Ricks United States 15 781 0.5× 542 0.6× 450 0.6× 434 1.6× 162 0.6× 21 1.8k
M. S. Berger United States 19 948 0.6× 857 0.9× 373 0.5× 342 1.2× 150 0.6× 37 2.1k
Marie Prewett United States 18 1.7k 1.1× 1.4k 1.5× 661 0.8× 274 1.0× 509 2.0× 34 2.9k
Gerhard Niederfellner Germany 22 847 0.6× 769 0.8× 710 0.9× 519 1.9× 83 0.3× 36 1.9k
Rajiv Bassi United States 19 783 0.5× 925 1.0× 288 0.4× 232 0.8× 280 1.1× 26 1.7k
Anne C. Pavlick United States 17 1.8k 1.2× 1.2k 1.3× 210 0.3× 236 0.9× 641 2.5× 57 2.4k
Xenia Jimenez United States 22 670 0.4× 1.2k 1.3× 630 0.8× 182 0.7× 280 1.1× 27 1.7k

Countries citing papers authored by Jan Pinkas

Since Specialization
Citations

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

Fields of papers citing papers by Jan Pinkas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jan Pinkas

This figure shows the co-authorship network connecting the top 25 collaborators of Jan Pinkas. A scholar is included among the top collaborators of Jan Pinkas 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 Jan Pinkas. Jan Pinkas 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
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Yin, Feng, Jan Pinkas, Biplab Das, et al.. (2023). Quantitation of total antibody (tAb) from antibody drug conjugate (ADC) PYX-201 in rat and monkey plasma using an enzyme-linked immunosorbent assay (ELISA) and its application in preclinical studies. Journal of Pharmaceutical and Biomedical Analysis. 233. 115452–115452. 10 indexed citations
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Yin, Feng, et al.. (2023). A sensitive and rapid LC-MS/MS assay for quantitation of free payload Aur0101 from antibody drug conjugate (ADC) PYX-201 in human plasma. Journal of Chromatography B. 1226. 123786–123786. 6 indexed citations
6.
Nicolazzi, Céline, Anne Caron, Alexia Tellier, et al.. (2020). An Antibody–Drug Conjugate Targeting MUC1-Associated Carbohydrate CA6 Shows Promising Antitumor Activities. Molecular Cancer Therapeutics. 19(8). 1660–1669. 23 indexed citations
7.
Ponte, Jose F., Leanne Lanieri, Eshita Khera, et al.. (2020). Antibody Co-Administration Can Improve Systemic and Local Distribution of Antibody–Drug Conjugates to Increase In Vivo Efficacy. Molecular Cancer Therapeutics. 20(1). 203–212. 29 indexed citations
8.
Kovtun, Yelena, Gregory E. Jones, Sharlene Adams, et al.. (2018). A CD123-targeting antibody-drug conjugate, IMGN632, designed to eradicate AML while sparing normal bone marrow cells. Blood Advances. 2(8). 848–858. 129 indexed citations
9.
Miller, Michael L., Nathan Fishkin, Wěi Li, et al.. (2016). A New Class of Antibody–Drug Conjugates with Potent DNA Alkylating Activity. Molecular Cancer Therapeutics. 15(8). 1870–1878. 65 indexed citations
10.
Ab, Olga, Kathleen R. Whiteman, Laura M. Bartle, et al.. (2015). IMGN853, a Folate Receptor-α (FRα)–Targeting Antibody–Drug Conjugate, Exhibits Potent Targeted Antitumor Activity against FRα-Expressing Tumors. Molecular Cancer Therapeutics. 14(7). 1605–1613. 160 indexed citations
11.
Kovtun, Yelena, Charlene A. Audette, Michele Mayo, et al.. (2010). Antibody-Maytansinoid Conjugates Designed to Bypass Multidrug Resistance. Cancer Research. 70(6). 2528–2537. 194 indexed citations
12.
Whiteman, Kathleen R., Holly A. Johnson, Shanqin Xu, et al.. (2009). Abstract #2799: Combination therapy with IMGN901 and lenalidomide plus low-dose dexamethasone is highly effective in multiple myeloma xenograft models. Cancer Research. 69. 2799–2799. 6 indexed citations
13.
Nam, Jeong‐Seok, Mi‐Jin Kang, Christina H. Stuelten, et al.. (2006). Bone Sialoprotein Mediates the Tumor Cell–Targeted Prometastatic Activity of Transforming Growth Factor β in a Mouse Model of Breast Cancer. Cancer Research. 66(12). 6327–6335. 72 indexed citations
14.
Pinkas, Jan & Beverly A. Teicher. (2006). TGF-β in cancer and as a therapeutic target. Biochemical Pharmacology. 72(5). 523–529. 45 indexed citations
15.
Martin, Stuart S., Alan G. Ridgeway, Jan Pinkas, et al.. (2004). A cytoskeleton-based functional genetic screen identifies Bcl-xL as an enhancer of metastasis, but not primary tumor growth. Oncogene. 23(26). 4641–4645. 54 indexed citations
16.
Pinkas, Jan, Stephen P. Naber, Janet S. Butel, Daniel Medina, & D. Joseph Jerry. (1999). Expression of MDM2 during mammary tumorigenesis. International Journal of Cancer. 81(2). 292–298. 18 indexed citations
17.
Jerry, D. Joseph, et al.. (1999). Regulation of p53 and Its Targets During Involution of the Mammary Gland. Journal of Mammary Gland Biology and Neoplasia. 4(2). 177–181. 15 indexed citations
18.
Eskenazi, Allen E., Jennifer G. Powers, Jan Pinkas, et al.. (1998). Induction of heat shock protein 27 by hydroxyurea and its relationship to experimental metastasis. Clinical & Experimental Metastasis. 16(3). 283–290. 9 indexed citations
19.
Jerry, D. Joseph, Charlotte Kuperwasser, Jan Pinkas, et al.. (1998). Delayed involution of the mammary epithelium in BALB/c-p53null mice. Oncogene. 17(18). 2305–2312. 76 indexed citations
20.
Eskenazi, Allen E., Jan Pinkas, John C. Whitin, et al.. (1993). Role of Antioxidant Enzymes in the Induction of Increased Experimental Metastasis by Hydroxyurea. JNCI Journal of the National Cancer Institute. 85(9). 711–721. 18 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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