Lisa Fitzgerald-Miller

549 total citations
7 papers, 269 citations indexed

About

Lisa Fitzgerald-Miller is a scholar working on Genetics, Surgery and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Lisa Fitzgerald-Miller has authored 7 papers receiving a total of 269 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Genetics, 3 papers in Surgery and 3 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Lisa Fitzgerald-Miller's work include Diabetes and associated disorders (6 papers), T-cell and B-cell Immunology (3 papers) and Immune Cell Function and Interaction (3 papers). Lisa Fitzgerald-Miller is often cited by papers focused on Diabetes and associated disorders (6 papers), T-cell and B-cell Immunology (3 papers) and Immune Cell Function and Interaction (3 papers). Lisa Fitzgerald-Miller collaborates with scholars based in United States, France and United Kingdom. Lisa Fitzgerald-Miller's co-authors include Peter A. Gottlieb, Aaron W. Michels, R. Wagner, Rocky L. Baker, Marian Rewers, Kathryn Haskins, Thomas Packard, Min Huang, Patrick C. Wilson and Mia J. Smith and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Diabetes and Biochemical and Biophysical Research Communications.

In The Last Decade

Lisa Fitzgerald-Miller

7 papers receiving 266 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lisa Fitzgerald-Miller United States 7 203 146 126 120 24 7 269
K. S. Rønningen Norway 6 227 1.1× 149 1.0× 117 0.9× 65 0.5× 31 1.3× 7 274
Ursule Van de Velde Belgium 9 334 1.6× 285 2.0× 274 2.2× 92 0.8× 18 0.8× 16 398
Vimukthi Pathiraja United States 8 234 1.2× 195 1.3× 134 1.1× 99 0.8× 58 2.4× 12 329
Alberto Pugliese United States 7 402 2.0× 347 2.4× 300 2.4× 106 0.9× 39 1.6× 11 494
Mylinh Dang United States 6 139 0.7× 104 0.7× 81 0.6× 58 0.5× 20 0.8× 8 173
Alexandra Fouts United States 8 240 1.2× 208 1.4× 163 1.3× 38 0.3× 58 2.4× 9 282
Mohamed M. Jahromi United States 8 223 1.1× 160 1.1× 156 1.2× 80 0.7× 30 1.3× 10 303
E. A. Johnson United States 7 297 1.5× 145 1.0× 82 0.7× 231 1.9× 36 1.5× 13 389
Terri Ning Canada 6 109 0.5× 73 0.5× 37 0.3× 234 1.9× 64 2.7× 9 365
Sandra Lord United States 10 138 0.7× 125 0.9× 131 1.0× 22 0.2× 26 1.1× 18 218

Countries citing papers authored by Lisa Fitzgerald-Miller

Since Specialization
Citations

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

Fields of papers citing papers by Lisa Fitzgerald-Miller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lisa Fitzgerald-Miller. 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 Lisa Fitzgerald-Miller. The network helps show where Lisa Fitzgerald-Miller may publish in the future.

Co-authorship network of co-authors of Lisa Fitzgerald-Miller

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

All Works

7 of 7 papers shown
1.
James, Eddie A., Joana R. F. Abreu, John W. McGinty, et al.. (2017). Combinatorial detection of autoreactive CD8+ T cells with HLA-A2 multimers: a multi-centre study by the Immunology of Diabetes Society T Cell Workshop. Diabetologia. 61(3). 658–670. 19 indexed citations
2.
Nakayama, Maki, Kristen A. McDaniel, Lisa Fitzgerald-Miller, et al.. (2015). Regulatory vs. inflammatory cytokine T-cell responses to mutated insulin peptides in healthy and type 1 diabetic subjects. Proceedings of the National Academy of Sciences. 112(14). 4429–4434. 59 indexed citations
3.
Smith, Mia J., Thomas Packard, Shannon O’Neill, et al.. (2014). Loss of Anergic B Cells in Prediabetic and New-Onset Type 1 Diabetic Patients. Diabetes. 64(5). 1703–1712. 79 indexed citations
4.
Gottlieb, Peter A., Thomas Delong, Rocky L. Baker, et al.. (2013). Chromogranin A is a T cell antigen in human type 1 diabetes. Journal of Autoimmunity. 50. 38–41. 67 indexed citations
5.
James, Eddie A., Roberto Mallone, Nanette C. Schloot, et al.. (2011). Immunology of Diabetes Society T‐Cell Workshop: HLA class II tetramer‐directed epitope validation initiative. Diabetes/Metabolism Research and Reviews. 27(8). 727–736. 20 indexed citations
6.
Mallone, Roberto, Eddie A. James, Lisa Fitzgerald-Miller, et al.. (2011). Immunology of Diabetes Society T‐Cell Workshop: HLA class I tetramer‐directed epitope validation initiative T‐Cell Workshop Report—HLA Class I Tetramer Validation Initiative. Diabetes/Metabolism Research and Reviews. 27(8). 720–726. 18 indexed citations
7.
Brodsky, Gary, et al.. (2007). The prelamin A pre-peptide induces cardiac and skeletal myoblast differentiation. Biochemical and Biophysical Research Communications. 356(4). 872–879. 7 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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