R GOOD

506 citations
8 papers · 420 · h-index 7

Impact in

    • Immunodeficiency and Autoimmune Disorders
    • Immune Cell Function and Interaction
  • Genetics top 10%
    • Bacterial Genetics and Biotechnology
    • Blood disorders and treatments

Papers in

    • Blood disorders and treatments 3
    • Bacterial Genetics and Biotechnology 2
    • Immunodeficiency and Autoimmune Disorders 2

R GOOD

8 papers receiving 368 citations

Peers

R GOOD
Comparison fields: 5 of 68
  • Immunology 141
  • Genetics 190
  • Molecular Medicine 27
  • Hematology 52
  • Endocrinology 22
Replace R D Sublett with:
R D Sublett United States
Robert A. Bonnah United States
Vivienne B. Gibson United Kingdom
Shuangyou Liu China
Gladys Tan Singapore
H. Dichtelmüller Germany
Lingxia Chen China
Emma Andersson Nordahl Sweden
Christine Ried Germany
H. A. Daniel Lagassé United States
R GOOD relative to R D Sublett United States R D Sublett's profile →
Citations per field
00.5×1.5×2×
R D Sublett · 1×
Citations per year

Countries citing papers authored by R GOOD

Since Specialization
Citations

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

Fields of papers citing papers by R GOOD

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside R GOOD, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with R GOOD Line = papers co-authored together R GOOD links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 1970131
2 1983123
3 198576
4 199635
5 199526
6 197915
7 197911
8 19963

About R GOOD

R GOOD is a scholar working on Genetics, Immunology, Molecular Biology, Hematology and Dermatology, having authored 8 papers that have together received 420 indexed citations. Recurring topics across this work include Blood disorders and treatments (3 papers), Immunodeficiency and Autoimmune Disorders (2 papers), Bacterial Genetics and Biotechnology (2 papers), RNA and protein synthesis mechanisms (2 papers), Cutaneous lymphoproliferative disorders research (1 paper), Genomics and Chromatin Dynamics (1 paper), Pneumocystis jirovecii pneumonia detection and treatment (1 paper) and Acute Myeloid Leukemia Research (1 paper). The work is most often cited by research in Immunology (141 citations), Genetics (190 citations), Molecular Medicine (27 citations), Hematology (52 citations) and Endocrinology (22 citations). R GOOD has collaborated with scholars based in United States and France. Frequent co-authors include Kathleen Postle, William J. Yount, Henry G. Kunkel, Robert Hong, M Séligmann, Richard F. Lockey, F Diamond, Mitchel J. Seleznick, Sudhir Gupta and Madhavan Nair. Their work appears in journals such as Journal of Allergy and Clinical Immunology, Blood, Proceedings of the National Academy of Sciences, Acta Paediatrica and Journal of Clinical Investigation.

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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