Song‐Gui Yang

14 papers receiving 394 citations

Peers

Song‐Gui Yang
Comparison fields: 5 of 57
  • Hematology 97
  • Genetics 67
  • Epidemiology 106
  • Cellular and Molecular Neuroscience 47
  • Sensory Systems 12
Replace Evelyne Defrêne with:
Evelyne Defrêne France
Jonathan Galeotti United States
Robert Monks United States
Christelle Nonne France
Paulina Podszywalow‐Bartnicka Poland
Katelyn Powell United States
Luc Lévesque Canada
Constanze Blume Germany
George C. Haralabopoulos Greece
Kaijun Di United States
Song‐Gui Yang relative to Evelyne Defrêne France Evelyne Defrêne's profile →
Citations per field
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Evelyne Defrêne · 1×
Citations per year

Countries citing papers authored by Song‐Gui Yang

Since Specialization
Citations

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

Fields of papers citing papers by Song‐Gui Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Song‐Gui Yang. 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 Song‐Gui Yang. The network helps show where Song‐Gui Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Song‐Gui Yang, 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 Song‐Gui Yang Line = papers co-authored together Song‐Gui Yang links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 200180
2 199353
3 199452
4 199251
5 199247
6 199233
7 200025
8 198918
9 199117
10 200310
11 19905
12 19915
13 19944
14 20212

About Song‐Gui Yang

Song‐Gui Yang is a scholar working on Molecular Biology, Epidemiology, Endocrinology, Diabetes and Metabolism, Hematology and Cellular and Molecular Neuroscience, having authored 14 papers that have together received 402 indexed citations. Recurring topics across this work include Growth Hormone and Insulin-like Growth Factors (4 papers), Congenital Heart Disease Studies (4 papers), Aortic Disease and Treatment Approaches (2 papers), Metabolism, Diabetes, and Cancer (2 papers), Blood Coagulation and Thrombosis Mechanisms (2 papers), Neuropeptides and Animal Physiology (2 papers), Sirtuins and Resveratrol in Medicine (1 paper) and Chronic Myeloid Leukemia Treatments (1 paper). The work is most often cited by research in Hematology (97 citations), Genetics (67 citations), Epidemiology (106 citations), Cellular and Molecular Neuroscience (47 citations) and Sensory Systems (12 citations). Song‐Gui Yang has collaborated with scholars based in Canada and United States. Frequent co-authors include Morley D. Hollenberg, Adebayo Laniyonu, Mahmoud Saifeddine, Morley D. Hollenberg, Graham J. Moore, J. William Gaynor, Jack Rychik, Thomas L. Spray, Marie M. Gleason and Christopher Zachary. Their work appears in journals such as Molecular Pharmacology, Canadian Journal of Physiology and Pharmacology, Journal of Pharmacology and Experimental Therapeutics, American Journal of Physiology-Gastrointestinal and Liver Physiology and Journal of Thoracic and Cardiovascular Surgery.

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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