Jami Dwyer

1.5k total citations · 1 hit paper
15 papers, 1.3k citations indexed

About

Jami Dwyer is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Jami Dwyer has authored 15 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Immunology, 5 papers in Oncology and 4 papers in Molecular Biology. Recurrent topics in Jami Dwyer's work include T-cell and B-cell Immunology (7 papers), Immune Cell Function and Interaction (5 papers) and Cytokine Signaling Pathways and Interactions (4 papers). Jami Dwyer is often cited by papers focused on T-cell and B-cell Immunology (7 papers), Immune Cell Function and Interaction (5 papers) and Cytokine Signaling Pathways and Interactions (4 papers). Jami Dwyer collaborates with scholars based in United States, Poland and Australia. Jami Dwyer's co-authors include Soren Impey, Shannon K. McWeeney, Hyunjoo Cha‐Molstad, Gail Mandel, Jeremy M. Boss, Gregory S. Yochum, S. McCorkle, Richard H. Goodman, John J. Dunn and Arthur A. Vandenbark and has published in prestigious journals such as Cell, Journal of Neuroscience and The Journal of Immunology.

In The Last Decade

Jami Dwyer

15 papers receiving 1.3k citations

Hit Papers

Defining the CREB Regulon 2004 2026 2011 2018 2004 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Defining the CREB Regulon
    2004 595 citations Soren Impey, S. McCorkle et al. Cell profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jami Dwyer United States 14 597 379 265 194 178 15 1.3k
Willie Mark United States 18 1.0k 1.7× 230 0.6× 519 2.0× 197 1.0× 234 1.3× 27 1.9k
Akihiro Fujikawa Japan 27 999 1.7× 463 1.2× 271 1.0× 99 0.5× 121 0.7× 44 1.9k
Gabriele Stumm Germany 15 631 1.1× 196 0.5× 349 1.3× 145 0.7× 129 0.7× 23 1.6k
María Dolores Gutiérrez‐López Spain 22 416 0.7× 281 0.7× 179 0.7× 125 0.6× 116 0.7× 34 1.3k
Tamar Paperna Israel 21 524 0.9× 143 0.4× 124 0.5× 242 1.2× 124 0.7× 48 1.2k
Antonio Campos‐Caro Spain 23 853 1.4× 428 1.1× 247 0.9× 65 0.3× 81 0.5× 54 1.5k
Pamela J. Voulalas United States 11 635 1.1× 229 0.6× 308 1.2× 123 0.6× 127 0.7× 15 1.2k
Lisa Kockeritz Canada 9 1.4k 2.4× 302 0.8× 563 2.1× 305 1.6× 234 1.3× 10 2.2k
Morito Sakaue Japan 18 1.1k 1.8× 324 0.9× 370 1.4× 260 1.3× 144 0.8× 32 1.9k
Bahri Karaçay United States 25 441 0.7× 173 0.5× 251 0.9× 86 0.4× 145 0.8× 46 1.2k

Countries citing papers authored by Jami Dwyer

Since Specialization
Citations

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

Fields of papers citing papers by Jami Dwyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jami Dwyer

This figure shows the co-authorship network connecting the top 25 collaborators of Jami Dwyer. A scholar is included among the top collaborators of Jami Dwyer 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 Jami Dwyer. Jami Dwyer 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.
Impey, Soren, S. McCorkle, Hyunjoo Cha‐Molstad, et al.. (2004). Defining the CREB Regulon. Cell. 119(7). 1041–1054. 595 indexed citations breakdown →
2.
Matejuk, Agata, Antony C. Bakke, Corwyn Hopke, et al.. (2004). Estrogen treatment induces a novel population of regulatory cells, which suppresses experimental autoimmune encephalomyelitis. Journal of Neuroscience Research. 77(1). 119–126. 39 indexed citations
3.
Impey, Soren, Hyunjoo Cha‐Molstad, Jami Dwyer, et al.. (2004). Defining the CREB RegulonA Genome-Wide Analysis of Transcription Factor Regulatory Regions. Cell. 119(7). 1041–1054. 20 indexed citations
4.
Simon, Julian A., J. Simon C. Arthur, Jami Dwyer, et al.. (2004). Mitogen- and Stress-Activated Protein Kinase 1 Mediates cAMP Response Element-Binding Protein Phosphorylation and Activation by Neurotrophins. Journal of Neuroscience. 24(18). 4324–4332. 188 indexed citations
5.
Ito, Atsushi, Agata Matejuk, Corwyn Hopke, et al.. (2003). Transfer of Severe Experimental Autoimmune Encephalomyelitis by IL-12- and IL-18-Potentiated T Cells Is Estrogen Sensitive. The Journal of Immunology. 170(9). 4802–4809. 35 indexed citations
6.
Matejuk, Agata, Corwyn Hopke, Jami Dwyer, et al.. (2003). CNS gene expression pattern associated with spontaneous experimental autoimmune encephalomyelitis. Journal of Neuroscience Research. 73(5). 667–678. 20 indexed citations
7.
Matejuk, Agata, Jami Dwyer, Corwyn Hopke, Arthur A. Vandenbark, & Halina Offner. (2003). 17Beta-estradiol treatment profoundly down-regulates gene expression in spinal cord tissue in mice protected from experimental autoimmune encephalomyelitis.. PubMed. 51(3). 185–93. 16 indexed citations
8.
Liu, Hong Yan, Abigail C. Buenafe, Agata Matejuk, et al.. (2002). Estrogen inhibition of EAE involves effects on dendritic cell function. Journal of Neuroscience Research. 70(2). 238–248. 134 indexed citations
9.
Matejuk, Agata, Abigail C. Buenafe, Jami Dwyer, et al.. (2002). Endogenous CD4+BV8S2− T cells from TG BV8S2+ donors confer complete protection against spontaneous experimental encephalomyelitis (Sp‐EAE) in TCR transgenic, RAG−/− mice. Journal of Neuroscience Research. 71(1). 89–103. 10 indexed citations
10.
Matejuk, Agata, Jami Dwyer, Alex Zamora, Arthur A. Vandenbark, & Halina Offner. (2002). Evaluation of the Effects of 17β-Estradiol (17β-E2) on Gene Expression in Experimental Autoimmune Encephalomyelitis Using DNA Microarray. Endocrinology. 143(1). 313–319. 51 indexed citations
11.
Ito, Atsushi, Abigail C. Buenafe, Agata Matejuk, et al.. (2002). Estrogen Inhibits Systemic T Cell Expression of TNF-α and Recruitment of TNF-α+ T Cells and Macrophages into the CNS of Mice Developing Experimental Encephalomyelitis. Clinical Immunology. 102(3). 275–282. 42 indexed citations
12.
Matejuk, Agata, et al.. (2002). Effects of cytokine deficiency on chemokine expression in CNS of mice with EAE. Journal of Neuroscience Research. 67(5). 680–688. 30 indexed citations
13.
Tedla, Nicodemus, Jami Dwyer, Philip G. Truskett, et al.. (1999). Phenotypic and functional characterization of lymphocytes derived from normal and HIV-1-infected human lymph nodes. Clinical & Experimental Immunology. 117(1). 92–99. 28 indexed citations
14.
Tedla, Nicodemus, Patricia Palladinetti, Mark Kelly, et al.. (1996). Chemokines and T lymphocyte recruitment to lymph nodes in HIV infection.. PubMed. 148(5). 1367–73. 64 indexed citations
15.
Colagiuri, Stephen, et al.. (1996). Intravenous immunoglobulin therapy for autoimmune diabetes mellitus.. PubMed. 14 Suppl 15. S93–7. 17 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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