Edith Palmieri

626 total citations
7 papers, 503 citations indexed

About

Edith Palmieri is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Edith Palmieri has authored 7 papers receiving a total of 503 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Immunology, 2 papers in Oncology and 1 paper in Molecular Biology. Recurrent topics in Edith Palmieri's work include T-cell and B-cell Immunology (5 papers), Immune Cell Function and Interaction (4 papers) and Immunotherapy and Immune Responses (4 papers). Edith Palmieri is often cited by papers focused on T-cell and B-cell Immunology (5 papers), Immune Cell Function and Interaction (4 papers) and Immunotherapy and Immune Responses (4 papers). Edith Palmieri collaborates with scholars based in United States, Chile and United Kingdom. Edith Palmieri's co-authors include Stanley G. Nathenson, Alexis M. Kalergis, Earl Goyarts, Zsuzsanna Végh, Immanuel F. Luescher, Marie‐Agnès Doucey, Nicole Boucheron, Byron Goldstein, Jorge E. Mora and Leandro J. Carreño and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Immunology and The Journal of Immunology.

In The Last Decade

Edith Palmieri

7 papers receiving 497 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Edith Palmieri United States	6	418	189	98	92	30	7	503
Xiang Zhao China	10	242 0.6×	152 0.8×	48 0.5×	86 0.9×	49 1.6×	19	380
Nicholas J. Pumphrey United Kingdom	8	292 0.7×	204 1.1×	46 0.5×	120 1.3×	22 0.7×	15	416
B.A. Van Krimpen Netherlands	11	350 0.8×	207 1.1×	108 1.1×	84 0.9×	22 0.7×	15	471
Leonardo Sibilio Italy	10	252 0.6×	114 0.6×	18 0.2×	90 1.0×	32 1.1×	13	371
Margit Haahr Hansen Denmark	6	198 0.5×	93 0.5×	205 2.1×	273 3.0×	11 0.4×	7	466
Jo Soden United States	9	88 0.2×	111 0.6×	64 0.7×	117 1.3×	18 0.6×	10	263
Eyal Kalie Israel	6	268 0.6×	184 1.0×	67 0.7×	137 1.5×	5 0.2×	6	430
Dianne H. Wilson United States	8	458 1.1×	73 0.4×	94 1.0×	101 1.1×	7 0.2×	11	540
Bee-Cheng Sim United States	11	364 0.9×	106 0.6×	111 1.1×	165 1.8×	15 0.5×	14	573
Galit Horn Israel	10	116 0.3×	127 0.7×	35 0.4×	186 2.0×	52 1.7×	18	291

Countries citing papers authored by Edith Palmieri

Since Specialization

Citations

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

Fields of papers citing papers by Edith Palmieri

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Edith Palmieri

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

All Works

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7 of 7 papers shown

Lázár‐Molnár, Eszter, Lisa Scandiuzzi, Indranil Basu, et al.. (2017). Structure-guided development of a high-affinity human Programmed Cell Death-1: Implications for tumor immunotherapy. EBioMedicine. 17. 30–44. 48 indexed citations

Qian, Jie, Irene Jarchum, Leann M. Mikesh, et al.. (2010). Predominant occupation of the class I MHC molecule H-2Kwm7 with a single self-peptide suggests a mechanism for its diabetes-protective effect. International Immunology. 22(3). 191–203. 3 indexed citations

González, Pablo A., Leandro J. Carreño, Daniel Coombs, et al.. (2005). T cell receptor binding kinetics required for T cell activation depend on the density of cognate ligand on the antigen-presenting cell. Proceedings of the National Academy of Sciences. 102(13). 4824–4829. 130 indexed citations

Ostrov, David A., Matthew M. Roden, Wuxian Shi, et al.. (2002). How H13 Histocompatibility Peptides Differing by a Single Methyl Group and Lacking Conventional MHC Binding Anchor Motifs Determine Self-Nonself Discrimination. The Journal of Immunology. 168(1). 283–289. 33 indexed citations

Kalergis, Alexis M., Nicole Boucheron, Marie‐Agnès Doucey, et al.. (2001). Efficient T cell activation requires an optimal dwell-time of interaction between the TCR and the pMHC complex. Nature Immunology. 2(3). 229–234. 253 indexed citations

Kalergis, Alexis M., Earl Goyarts, Edith Palmieri, et al.. (2000). A simplified procedure for the preparation of MHC/peptide tetramers: chemical biotinylation of an unpaired cysteine engineered at the C-terminus of MHC-I. Journal of Immunological Methods. 234(1-2). 61–70. 26 indexed citations

Hörig, Heidi, Nicholas Papadopoulos, Zsuzsanna Végh, et al.. (1997). Anin vitrostudy of the dynamic features of the major histocompatibility complex class I complex relevant to its role as a versatile peptide-receptive molecule. Proceedings of the National Academy of Sciences. 94(25). 13826–13831. 10 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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