Jonathan M. Shaffer

1.6k total citations
16 papers, 654 citations indexed

About

Jonathan M. Shaffer is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Jonathan M. Shaffer has authored 16 papers receiving a total of 654 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 7 papers in Cancer Research and 2 papers in Oncology. Recurrent topics in Jonathan M. Shaffer's work include MicroRNA in disease regulation (7 papers), Extracellular vesicles in disease (5 papers) and RNA modifications and cancer (4 papers). Jonathan M. Shaffer is often cited by papers focused on MicroRNA in disease regulation (7 papers), Extracellular vesicles in disease (5 papers) and RNA modifications and cancer (4 papers). Jonathan M. Shaffer collaborates with scholars based in United States, Germany and United Kingdom. Jonathan M. Shaffer's co-authors include Eric Lader, Markus Sprenger‐Haussels, Martin Schlumpberger, Johan Skog, Daniel Enderle, Alexandra Spiel, Christine M. Coticchia, Mikkel Noerholm, Romy Mueller and Emily G. Berghoff and has published in prestigious journals such as Molecular Cell, Bioinformatics and PLoS ONE.

In The Last Decade

Jonathan M. Shaffer

15 papers receiving 648 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jonathan M. Shaffer United States	9	522	398	64	48	36	16	654
Guanyi Kong China	8	480 0.9×	348 0.9×	150 2.3×	39 0.8×	16 0.4×	9	754
Cui Li China	15	409 0.8×	311 0.8×	70 1.1×	60 1.3×	16 0.4×	47	657
Sabine Kafert-Kasting Germany	7	280 0.5×	154 0.4×	27 0.4×	29 0.6×	26 0.7×	10	431
Huilin Jin China	15	276 0.5×	140 0.4×	100 1.6×	39 0.8×	8 0.2×	26	508
Xiaopin Ji China	15	301 0.6×	183 0.5×	197 3.1×	69 1.4×	6 0.2×	31	584
Aichun Li China	6	539 1.0×	360 0.9×	29 0.5×	75 1.6×	11 0.3×	8	641
Hiroko Mataki Japan	14	594 1.1×	545 1.4×	70 1.1×	42 0.9×	4 0.1×	16	779
Chenlei Wen China	11	434 0.8×	401 1.0×	131 2.0×	48 1.0×	5 0.1×	26	622
Hongxia Li China	14	337 0.6×	247 0.6×	126 2.0×	62 1.3×	6 0.2×	39	575
Jonas J. Staudacher United States	11	359 0.7×	153 0.4×	321 5.0×	57 1.2×	6 0.2×	25	693

Countries citing papers authored by Jonathan M. Shaffer

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan M. Shaffer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan M. Shaffer

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

All Works

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16 of 16 papers shown

Deng, Jixin, Jingxiao Zhang, John DiCarlo, et al.. (2025). UMI-nea: a fast, robust tool for reference-free UMI deduplication and accurate quantification. Bioinformatics. 41(9).

Babayan, Anna, Martin H. Neumann, Andrei Herdean, et al.. (2020). Multicenter Evaluation of Independent High-Throughput and RT-qPCR Technologies for the Development of Analytical Workflows for Circulating miRNA Analysis. Cancers. 12(5). 1166–1166. 16 indexed citations

Simon, Sylvain, Virginie Vignard, Jean-Baptiste Alberge, et al.. (2020). Increased antitumor efficacy of PD-1-deficient melanoma-specific human lymphocytes. Journal for ImmunoTherapy of Cancer. 8(1). e000311–e000311. 25 indexed citations

Kloten, Vera, Martin H. Neumann, Francesca Di Pasquale, et al.. (2019). Multicenter Evaluation of Circulating Plasma MicroRNA Extraction Technologies for the Development of Clinically Feasible Reverse Transcription Quantitative PCR and Next-Generation Sequencing Analytical Work Flows. Clinical Chemistry. 65(9). 1132–1140. 41 indexed citations

Bluhm, Leonie, Rüdiger Greinert, Beate Volkmer, et al.. (2018). Abstract 5596: Impact of blood collection tubes on CTC-, ctDNA- and miRNA recoveries in malignant melanoma patients. Cancer Research. 78(13_Supplement). 5596–5596. 1 indexed citations

Shaffer, Jonathan M., et al.. (2018). Attending a social event and consuming alcohol is associated with changes in serum microRNA: a before and after study in healthy adults. Biomarkers. 23(8). 781–786. 10 indexed citations

Enderle, Daniel, Alexandra Spiel, Christine M. Coticchia, et al.. (2015). Characterization of RNA from Exosomes and Other Extracellular Vesicles Isolated by a Novel Spin Column-Based Method. PLoS ONE. 10(8). e0136133–e0136133. 305 indexed citations

Vliegenthart, A. D. Bastiaan, Jonathan M. Shaffer, Joanna I. Clarke, et al.. (2015). Comprehensive microRNA profiling in acetaminophen toxicity identifies novel circulating biomarkers for human liver and kidney injury. Scientific Reports. 5(1). 15501–15501. 102 indexed citations

Janas, Maja M., Bingbing Wang, Mike Aguiar, et al.. (2012). Alternative RISC assembly: Binding and repression of microRNA–mRNA duplexes by human Ago proteins. RNA. 18(11). 2041–2055. 99 indexed citations

10.

Janas, Maja M., Eric T. Wang, Tara M. Love, et al.. (2012). Reduced Expression of Ribosomal Proteins Relieves MicroRNA-Mediated Repression. Molecular Cell. 46(2). 171–186. 22 indexed citations

11.

Shaffer, Jonathan M., Xiujing Gu, & Kathy Boon. (2010). Abstract 152: Methylated-DNA enrichment and PCR array technology: A qPCR-based method to rapidly screen for changes in promoter DNA methylation patterns. Cancer Research. 70(8_Supplement). 152–152. 1 indexed citations

12.

Shaffer, Jonathan M., Sabine Hellwig, & Thomas E. Smithgall. (2009). Bimolecular Fluorescence Complementation Demonstrates That the c-Fes Protein−Tyrosine Kinase Forms Constitutive Oligomers in Living Cells. Biochemistry. 48(22). 4780–4788. 6 indexed citations

13.

Shaffer, Jonathan M. & Thomas E. Smithgall. (2008). Promoter methylation blocks FES protein‐tyrosine kinase gene expression in colorectal cancer. Genes Chromosomes and Cancer. 48(3). 272–284. 15 indexed citations

14.

Delfino, Frank J., Jonathan M. Shaffer, & Thomas E. Smithgall. (2006). The KRAB-associated co-repressor KAP-1 is a coiled-coil binding partner, substrate and activator of the c-Fes protein tyrosine kinase. Biochemical Journal. 399(1). 141–150. 6 indexed citations

15.

Karandikar, Umesh C., Jonathan M. Shaffer, Clifton P. Bishop, & Ashok P. Bidwai. (2005). Drosophila CK2 phosphorylates Deadpan, a member of the HES family of basic-helix-loop-helix (bHLH) repressors. Molecular and Cellular Biochemistry. 274(1-2). 133–139. 4 indexed citations

16.

Shaffer, Jonathan M. & N. P. Bigelow. (1996). Two-species trap experiments. Quantum Electronics and Laser Science Conference. 1 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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