Fred M. Kaplan

654 total citations
9 papers, 487 citations indexed

About

Fred M. Kaplan is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Fred M. Kaplan has authored 9 papers receiving a total of 487 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 Computational Theory and Mathematics. Recurrent topics in Fred M. Kaplan's work include Computational Drug Discovery Methods (3 papers), Cancer-related gene regulation (3 papers) and Melanoma and MAPK Pathways (3 papers). Fred M. Kaplan is often cited by papers focused on Computational Drug Discovery Methods (3 papers), Cancer-related gene regulation (3 papers) and Melanoma and MAPK Pathways (3 papers). Fred M. Kaplan collaborates with scholars based in United States and Italy. Fred M. Kaplan's co-authors include Yongping Shao, Andrew E. Aplin, Anthony J. Capobianco, Andrew E. Aplin, Antonio Rosato, Marco Napoli, Luca Tiberi, Alessia Soldano, Salvatore Pece and Paolo Nucíforo and has published in prestigious journals such as Journal of Biological Chemistry, The Journal of Immunology and PLoS ONE.

In The Last Decade

Fred M. Kaplan

9 papers receiving 484 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fred M. Kaplan United States	8	383	208	124	45	44	9	487
Sumit Deswal Germany	10	330 0.9×	147 0.7×	119 1.0×	22 0.5×	37 0.8×	12	478
Curtis H. Kugel United States	8	382 1.0×	239 1.1×	69 0.6×	51 1.1×	48 1.1×	8	461
Tammy Sobolik United States	8	210 0.5×	213 1.0×	103 0.8×	23 0.5×	56 1.3×	9	386
Aida Shahrabi Netherlands	6	308 0.8×	239 1.1×	118 1.0×	33 0.7×	47 1.1×	7	463
Janice M. Mehnert United States	8	303 0.8×	183 0.9×	52 0.4×	40 0.9×	28 0.6×	24	409
Hélène Malka-Mahieu France	6	387 1.0×	136 0.7×	45 0.4×	31 0.7×	29 0.7×	10	456
Joanne M. Munck United Kingdom	10	484 1.3×	180 0.9×	47 0.4×	40 0.9×	47 1.1×	19	556
Tristan Gallenne Netherlands	8	458 1.2×	206 1.0×	89 0.7×	18 0.4×	28 0.6×	14	633
E. P. Reddy United States	6	201 0.5×	216 1.0×	147 1.2×	26 0.6×	15 0.3×	11	483
Loren Lasko United States	11	288 0.8×	196 0.9×	67 0.5×	22 0.5×	30 0.7×	13	421

Countries citing papers authored by Fred M. Kaplan

Since Specialization

Citations

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

Fields of papers citing papers by Fred M. Kaplan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fred M. Kaplan

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

All Works

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9 of 9 papers shown

Chornoguz, Olesya, Karen Leander, Fred M. Kaplan, et al.. (2017). TIM-3 Engagement Promotes Effector Memory T Cell Differentiation of Human Antigen-Specific CD8 T Cells by Activating mTORC1. The Journal of Immunology. 199(12). 4091–4102. 30 indexed citations

Tomkowicz, Brian, Eileen S. Walsh, Raluca Verona, et al.. (2015). TIM-3 Suppresses Anti-CD3/CD28-Induced TCR Activation and IL-2 Expression through the NFAT Signaling Pathway. PLoS ONE. 10(10). e0140694–e0140694. 57 indexed citations

Kaplan, Fred M., et al.. (2012). SHOC2 and CRAF Mediate ERK1/2 Reactivation in Mutant NRAS-mediated Resistance to RAF Inhibitor. Journal of Biological Chemistry. 287(50). 41797–41807. 47 indexed citations

Aplin, Andrew E., Fred M. Kaplan, & Yongping Shao. (2011). Mechanisms of Resistance to RAF Inhibitors in Melanoma. Journal of Investigative Dermatology. 131(9). 1817–1820. 62 indexed citations

Ranganathan, Prathibha, Rodrigo Vasquez‐Del Carpio, Fred M. Kaplan, et al.. (2011). Hierarchical Phosphorylation within the Ankyrin Repeat Domain Defines a Phosphoregulatory Loop That Regulates Notch Transcriptional Activity. Journal of Biological Chemistry. 286(33). 28844–28857. 32 indexed citations

Carpio, Rodrigo Vasquez‐Del, Fred M. Kaplan, Kelly Weaver, et al.. (2011). Assembly of a Notch Transcriptional Activation Complex Requires Multimerization. Molecular and Cellular Biology. 31(7). 1396–1408. 27 indexed citations

Kaplan, Fred M., et al.. (2010). Hyperactivation of MEK–ERK1/2 signaling and resistance to apoptosis induced by the oncogenic B-RAF inhibitor, PLX4720, in mutant N-RAS melanoma cells. Oncogene. 30(3). 366–371. 82 indexed citations

Rustighi, Alessandra, Luca Tiberi, Alessia Soldano, et al.. (2009). The prolyl-isomerase Pin1 is a Notch1 target that enhances Notch1 activation in cancer. Nature Cell Biology. 11(2). 133–142. 146 indexed citations

Frank, Richard C., et al.. (2005). Response of carcinoma of unknown primary site affecting bone to thalidomide. The Lancet Oncology. 6(7). 534–535. 4 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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