Gene Barbour

1.4k total citations · 1 hit paper
15 papers, 1.2k citations indexed

About

Gene Barbour is a scholar working on Genetics, Surgery and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Gene Barbour has authored 15 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Genetics, 7 papers in Surgery and 5 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Gene Barbour's work include Diabetes and associated disorders (11 papers), Pancreatic function and diabetes (7 papers) and T-cell and B-cell Immunology (4 papers). Gene Barbour is often cited by papers focused on Diabetes and associated disorders (11 papers), Pancreatic function and diabetes (7 papers) and T-cell and B-cell Immunology (4 papers). Gene Barbour collaborates with scholars based in United States. Gene Barbour's co-authors include Kathryn Haskins, Thomas Delong, Brenda Bradley, Rocky L. Baker, Roger Powell, Nichole Reisdorph, Michael Armstrong, Richard Reisdorph, Timothy A. Wiles and Jing He and has published in prestigious journals such as Science, Nature Immunology and The Journal of Immunology.

In The Last Decade

Gene Barbour

15 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion

2016 379 citations Thomas Delong, Timothy A. Wiles et al. Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gene Barbour United States	13	841	560	518	422	143	15	1.2k
Ivana Durinovic‐Belló Germany	20	720 0.9×	494 0.9×	475 0.9×	414 1.0×	96 0.7×	37	1.0k
Ann Avron Israel	8	382 0.5×	316 0.6×	203 0.4×	307 0.7×	165 1.2×	8	678
Michele P. Marron United States	16	619 0.7×	354 0.6×	412 0.8×	237 0.6×	104 0.7×	21	804
Isabelle Bergerot France	12	464 0.6×	273 0.5×	457 0.9×	221 0.5×	156 1.1×	14	866
Bryan Coon United States	13	453 0.5×	217 0.4×	566 1.1×	166 0.4×	93 0.7×	15	901
Mériam Belghith Tunisia	7	375 0.4×	188 0.3×	503 1.0×	144 0.3×	59 0.4×	13	794
Laure Caccavelli France	13	146 0.2×	112 0.2×	406 0.8×	254 0.6×	204 1.4×	20	905
Mia Westerholm‐Ormio Finland	14	363 0.4×	184 0.3×	270 0.5×	59 0.1×	198 1.4×	19	789
Zuoan Yi United States	18	148 0.2×	122 0.2×	671 1.3×	47 0.1×	192 1.3×	24	858
Serge Durviaux Belgium	10	312 0.4×	339 0.6×	27 0.1×	132 0.3×	384 2.7×	13	703

Countries citing papers authored by Gene Barbour

Since Specialization

Citations

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

Fields of papers citing papers by Gene Barbour

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gene Barbour

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

All Works

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15 of 15 papers shown

Wenzlau, Janet M., Gene Barbour, Mylinh Dang, et al.. (2022). Insulin B-chain hybrid peptides are agonists for T cells reactive to insulin B:9-23 in autoimmune diabetes. Frontiers in Immunology. 13. 926650–926650. 11 indexed citations

Wiles, Timothy A., Janet M. Wenzlau, Roger Powell, et al.. (2022). Cathepsin D Drives the Formation of Hybrid Insulin Peptides Relevant to the Pathogenesis of Type 1 Diabetes. Diabetes. 71(12). 2793–2803. 33 indexed citations

Baker, Rocky L., Timothy A. Wiles, Robin S. Lindsay, et al.. (2018). CD4 T Cells Reactive to Hybrid Insulin Peptides Are Indicators of Disease Activity in the NOD Mouse. Diabetes. 67(9). 1836–1846. 53 indexed citations

Delong, Thomas, Timothy A. Wiles, Rocky L. Baker, et al.. (2016). Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion. Science. 351(6274). 711–714. 379 indexed citations breakdown →

Wiles, Timothy A., Thomas Delong, Rocky L. Baker, et al.. (2016). An insulin-IAPP hybrid peptide is an endogenous antigen for CD4 T cells in the non-obese diabetic mouse. Journal of Autoimmunity. 78. 11–18. 75 indexed citations

Baker, Rocky L., Brenda Bradley, Timothy A. Wiles, et al.. (2015). Cutting Edge: Nonobese Diabetic Mice Deficient in Chromogranin A Are Protected from Autoimmune Diabetes. The Journal of Immunology. 196(1). 39–43. 40 indexed citations

Baker, Rocky L., Thomas Delong, Gene Barbour, et al.. (2013). Cutting Edge: CD4 T Cells Reactive to an Islet Amyloid Polypeptide Peptide Accumulate in the Pancreas and Contribute to Disease Pathogenesis in Nonobese Diabetic Mice. The Journal of Immunology. 191(8). 3990–3994. 32 indexed citations

Delong, Thomas, Rocky L. Baker, Jing He, et al.. (2012). Diabetogenic T-Cell Clones Recognize an Altered Peptide of Chromogranin A. Diabetes. 61(12). 3239–3246. 90 indexed citations

Delong, Thomas, Rocky L. Baker, Nichole Reisdorph, et al.. (2011). Islet Amyloid Polypeptide Is a Target Antigen for Diabetogenic CD4+ T Cells. Diabetes. 60(9). 2325–2330. 45 indexed citations

10.

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

11.

He, Jing, et al.. (2008). Regulatory T Cells Prevent Transfer of Type 1 Diabetes in NOD Mice Only When Their Antigen Is Present In Vivo. The Journal of Immunology. 181(7). 4516–4522. 56 indexed citations

12.

Roberts, Samantha, et al.. (2003). Adoptive transfer of islet antigen–autoreactive T cell clones to transgenic NOD.Eadmice induces diabetes indicating a lack of I-E mediated protection against activated effector T cells. Journal of Autoimmunity. 21(2). 139–147. 3 indexed citations

13.

Burns, William H., Gene Barbour, & Gordon Sandford. (1988). Molecular cloning and mapping of rat cytomegalovirus DNA. Virology. 166(1). 140–148. 20 indexed citations

14.

Aurelian, Laure, Irving I. Kessler, Neil B. Rosenshein, & Gene Barbour. (1981). Viruses and gynecologic cancers: Herpesvirus protein (ICP 10/AG-4), a cervical tumor antigen that fulfills the criteria for a marker of carcinogenicity. Cancer. 48(S1). 455–471. 30 indexed citations

15.

Sheridan, John F., Laure Aurelian, Gene Barbour, et al.. (1981). Traveler's diarrhea associated with rotavirus infection: analysis of virus-specific immunoglobulin classes. Infection and Immunity. 31(1). 419–429. 14 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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