Michael Grow

949 citations
23 papers · 758 · h-index 14

Impact in

Papers in

    • Lipoproteins and Cardiovascular Health 3
    • Genetic Associations and Epidemiology 4
    • Hemoglobinopathies and Related Disorders 2

Michael Grow

23 papers receiving 718 citations

Peers

Michael Grow
Comparison fields: 5 of 94
  • Hematology 85
  • Endocrinology, Diabetes and Metabolism 116
  • Genetics 69
  • Clinical Biochemistry 40
  • Cardiology and Cardiovascular Medicine 96
Replace S J Lauer with:
S J Lauer United States
Hiroyuki Ishiguro Japan
R. Sartorio Italy
Bhupinder Bharaj Canada
Samad Barzegar United States
Nitya Nathwani United States
Rémi Piedagnel France
C.J.I. Raats Netherlands
Courtenay B. Barlow United States
Po‐Han Lin Taiwan
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Citations per year

Countries citing papers authored by Michael Grow

Since Specialization
Citations

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

Fields of papers citing papers by Michael Grow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999191
2 200778
3 200565
4 200260
5 200256
6 200247
7 199843
8 200142
9 198928
10 200126
11 200423
12 201022
13 200520
14 200016
15 200910
16 19997
17 19826
18 19825
19
Candidate gene polymorphism in cardiovascular disease: the BIP cohort.
20063
20 19963

About Michael Grow

Michael Grow is a scholar working on Surgery, Genetics, Molecular Biology, Sociology and Political Science and Political Science and International Relations, having authored 23 papers that have together received 758 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (4 papers), Lipoproteins and Cardiovascular Health (3 papers), Brazilian History and Foreign Policy (2 papers), Paraoxonase enzyme and polymorphisms (2 papers), Nuclear Receptors and Signaling (2 papers), Hormonal Regulation and Hypertension (2 papers), Hemoglobinopathies and Related Disorders (2 papers) and Bioinformatics and Genomic Networks (2 papers). The work is most often cited by research in Hematology (85 citations), Endocrinology, Diabetes and Metabolism (116 citations), Genetics (69 citations), Clinical Biochemistry (40 citations) and Cardiology and Cardiovascular Medicine (96 citations). Michael Grow has collaborated with scholars based in United States, France and Switzerland. Frequent co-authors include Suzanne Cheng, William Klitz, Gérard Siest, Céline Pallaud, Clive R. Pullinger, John P. Kane, Mary J. Malloy, Lori Steiner, Stephen G. Rabe and Sophia Visvikis. Their work appears in journals such as The American Historical Review, Journal of Lipid Research, European Journal of Human Genetics, Clinical Chemistry and Laboratory Medicine (CCLM) and Journal of Forensic Sciences.

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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