Michelle Gray

1.8k citations
17 papers · 1.0k · h-index 10

Impact in

Papers in

Michelle Gray

14 papers receiving 1.0k citations

Peers

Michelle Gray
Comparison fields: 5 of 73
  • Cellular and Molecular Neuroscience 675
  • Developmental Neuroscience 80
  • Neurology 211
  • Neurology 104
  • Molecular Biology 716
Replace Vahri Beaumont with:
Vahri Beaumont United States
Yijun Cui United States
Shalaka Mulherkar United States
Olga Yarygina United States
Panayiotis Tsokas United States
Jessica L. Saulnier United States
Karen Brami‐Cherrier France
R. Brusa Italy
Maciej Figiel Poland
David Soto Spain
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Citations per field
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Citations per year

Countries citing papers authored by Michelle Gray

Since Specialization
Citations

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

Fields of papers citing papers by Michelle Gray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 2006326
2 2012219
3 2014149
4 201082
5 201869
6 201367
7 201137
8 201923
9 201317
10 201912
11 20216
12 20205
13 20234
14 20241
15
A review of Data linkage procedures at NatCen
20101
16 20250
17 20250

About Michelle Gray

Michelle Gray is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology, Neurology, Automotive Engineering and Cardiology and Cardiovascular Medicine, having authored 17 papers that have together received 1.0k indexed citations. Recurring topics across this work include Genetic Neurodegenerative Diseases (12 papers), Mitochondrial Function and Pathology (8 papers), Neuroscience and Neuropharmacology Research (5 papers), Neurological disorders and treatments (4 papers), Older Adults Driving Studies (1 paper), Neurogenesis and neuroplasticity mechanisms (1 paper), Cardiac electrophysiology and arrhythmias (1 paper) and Cardiovascular Effects of Exercise (1 paper). The work is most often cited by research in Cellular and Molecular Neuroscience (675 citations), Developmental Neuroscience (80 citations), Neurology (211 citations), Neurology (104 citations) and Molecular Biology (716 citations). Michelle Gray has collaborated with scholars based in United States, Croatia and Canada. Frequent co-authors include Daniel H. Geschwind, X. William Yang, Stanislav L. Karsten, Mary Kay Lobo, Erin R. Greiner, Dyna Shirasaki, Carlos Cepeda, Michael S. Levine, Joseph A. Loo and Steve Horvath. Their work appears in journals such as Human Molecular Genetics, Neurobiology of Disease, Experimental Neurology, Transportation Research Part F Traffic Psychology and Behaviour and Neurotherapeutics.

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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