Frances Crawford

5.2k total citations
61 papers, 4.3k citations indexed

About

Frances Crawford is a scholar working on Immunology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Frances Crawford has authored 61 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Immunology, 14 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Molecular Biology. Recurrent topics in Frances Crawford's work include T-cell and B-cell Immunology (31 papers), Immune Cell Function and Interaction (27 papers) and Immunotherapy and Immune Responses (19 papers). Frances Crawford is often cited by papers focused on T-cell and B-cell Immunology (31 papers), Immune Cell Function and Interaction (27 papers) and Immunotherapy and Immune Responses (19 papers). Frances Crawford collaborates with scholars based in United States, China and France. Frances Crawford's co-authors include Philippa Marrack, John W. Kappler, Janice White, Shaodong Dai, Eric S. Huseby, Haruo Kozono, Brian D. Stadinski, George S. Eisenbarth, Michael T. Falta and James Scott‐Browne and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Frances Crawford

61 papers receiving 4.2k citations

Peers

Frances Crawford
Åke Lundwall Sweden
B. J. Fowlkes United States
Martha Ladner United States
Claude Perreault Canada
Ronald Palacios Sweden
Renu Jain United States
Akio Matsuzawa Japan
Ada M. Kruisbeek Netherlands
Valerie Quarmby United States
Roberto Tosi Italy
Åke Lundwall Sweden
Frances Crawford
Citations per year, relative to Frances Crawford Frances Crawford (= 1×) peers Åke Lundwall

Countries citing papers authored by Frances Crawford

Since Specialization
Citations

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

Fields of papers citing papers by Frances Crawford

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Frances Crawford

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

All Works

20 of 20 papers shown
1.
Falta, Michael T., Jeremy Chase Crawford, Laurie G. Landry, et al.. (2021). Beryllium-specific CD4+ T cells induced by chemokine neoantigens perpetuate inflammation. Journal of Clinical Investigation. 131(9). 11 indexed citations
2.
Crawford, Frances, Ryan C. Hill, Niyun Jin, et al.. (2020). Lysosomal cathepsin creates chimeric epitopes for diabetogenic CD4 T cells via transpeptidation. The Journal of Experimental Medicine. 218(2). 46 indexed citations
3.
Wang, Yang, Tomasz Sosinowski, Frances Crawford, et al.. (2019). How C-terminal additions to insulin B-chain fragments create superagonists for T cells in mouse and human type 1 diabetes. Science Immunology. 4(34). 40 indexed citations
4.
Jin, Niyun, Yang Wang, Frances Crawford, et al.. (2015). N-terminal additions to the WE14 peptide of chromogranin A create strong autoantigen agonists in type 1 diabetes. Proceedings of the National Academy of Sciences. 112(43). 13318–13323. 42 indexed citations
5.
McKee, Ailsa S., Douglas G. Mack, Frances Crawford, & Andrew P. Fontenot. (2015). MyD88 dependence of beryllium-induced dendritic cell trafficking and CD4+ T-cell priming. Mucosal Immunology. 8(6). 1237–1247. 27 indexed citations
6.
Falta, Michael T., Douglas G. Mack, Frances Crawford, et al.. (2015). Metal-specific CD4+ T-cell responses induced by beryllium exposure in HLA-DP2 transgenic mice. Mucosal Immunology. 9(1). 218–228. 11 indexed citations
7.
Zhang, Li, Frances Crawford, Liping Yu, et al.. (2014). Monoclonal antibody blocking the recognition of an insulin peptide–MHC complex modulates type 1 diabetes. Proceedings of the National Academy of Sciences. 111(7). 2656–2661. 56 indexed citations
8.
David, Alexandria, Frances Crawford, Paul Garside, et al.. (2014). Tolerance induction in memory CD4 T cells requires two rounds of antigen-specific activation. Proceedings of the National Academy of Sciences. 111(21). 7735–7740. 15 indexed citations
9.
Clayton, Gina M., Yang Wang, Frances Crawford, et al.. (2014). Structural Basis of Chronic Beryllium Disease: Linking Allergic Hypersensitivity and Autoimmunity. Cell. 158(1). 132–142. 87 indexed citations
10.
Falta, Michael T., Clemencia Pinilla, Douglas G. Mack, et al.. (2013). Identification of beryllium-dependent peptides recognized by CD4+ T cells in chronic beryllium disease. The Journal of Experimental Medicine. 210(7). 1403–1418. 49 indexed citations
11.
Yin, Lei, Frances Crawford, Philippa Marrack, John W. Kappler, & Shaodong Dai. (2012). T-cell receptor (TCR) interaction with peptides that mimic nickel offers insight into nickel contact allergy. Proceedings of the National Academy of Sciences. 109(45). 18517–18522. 39 indexed citations
12.
Stadinski, Brian D., Li Zhang, Frances Crawford, et al.. (2010). Diabetogenic T cells recognize insulin bound to IA g7 in an unexpected, weakly binding register. Proceedings of the National Academy of Sciences. 107(24). 10978–10983. 164 indexed citations
13.
Stadinski, Brian D., Thomas Delong, Nichole Reisdorph, et al.. (2010). Chromogranin A is an autoantigen in type 1 diabetes. Nature Immunology. 11(3). 225–231. 286 indexed citations
14.
Dai, Shaodong, Eric S. Huseby, Kira Rubtsova, et al.. (2008). Crossreactive T Cells Spotlight the Germline Rules for αβ T Cell-Receptor Interactions with MHC Molecules. Immunity. 28(3). 324–334. 136 indexed citations
15.
Peyerl, Fred W., Shaodong Dai, Guinevere A. Murphy, et al.. (2006). Elucidation of some Bax conformational changes through crystallization of an antibody–peptide complex. Cell Death and Differentiation. 14(3). 447–452. 43 indexed citations
16.
Huseby, Eric S., Frances Crawford, Janice White, Philippa Marrack, & John W. Kappler. (2006). Interface-disrupting amino acids establish specificity between T cell receptors and complexes of major histocompatibility complex and peptide. Nature Immunology. 7(11). 1191–1199. 83 indexed citations
17.
Huseby, Eric S., Janice White, Frances Crawford, et al.. (2005). How the T Cell Repertoire Becomes Peptide and MHC Specific. Cell. 122(2). 247–260. 251 indexed citations
18.
Liu, Xinqi, et al.. (2002). Alternate interactions define the binding of peptides to the MHC molecule IA b. Proceedings of the National Academy of Sciences. 99(13). 8820–8825. 62 indexed citations
19.
Lang, Paul, Benjamin A. Freiberg, Frances Crawford, et al.. (2001). TCR-Induced Transmembrane Signaling by Peptide/MHC Class II Via Associated Ig-α/β Dimers. Science. 291(5508). 1537–1540. 96 indexed citations
20.

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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