Maya Margalit

43 papers receiving 1.1k citations

Maya Margalit's Hit Papers

Randomized, Controlled Trial of the FGF21 Analogue Pegozafermin in NASH 2023 · 314 citations
3140+1+2Years since publication100200300

Peers

Maya Margalit
Comparison fields: 5 of 75
  • Epidemiology 443
  • Hepatology 100
  • Immunology 251
  • Endocrinology, Diabetes and Metabolism 163
  • Cell Biology 94
Replace Daniel A. Giles with:
Daniel A. Giles United States
Nadine Gehrke Germany
Kimberly Viker United States
Kotaro Kumagai Japan
S. Galastri Italy
Tomer Adar Israel
Sandy Olson United States
Seiichi Mawatari Japan
Zhongmou Jin United States
Soonil Kwon South Korea
Maya Margalit relative to Daniel A. Giles United States Daniel A. Giles's profile →
Citations per field
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Citations per year

Countries citing papers authored by Maya Margalit

Since Specialization
Citations

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

Fields of papers citing papers by Maya Margalit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Randomized, Controlled Trial of the FGF21 Analogue Pegozafermin in NASH
Hit paper breakdown →
2023314
2 200692
3 200681
4 202273
5 200664
6 200562
7 200447
8 200542
9 200335
10 200735
11 200534
12 200626
13 200125
14 202421
15 200419
16 200917
17 201017
18 201617
19 202316
20 200516

About Maya Margalit

Maya Margalit is a scholar working on Epidemiology, Molecular Biology, Surgery, Immunology and Physiology, having authored 48 papers that have together received 1.1k indexed citations. Recurring topics across this work include Liver Disease Diagnosis and Treatment (16 papers), Fibroblast Growth Factor Research (13 papers), Immune Cell Function and Interaction (10 papers), T-cell and B-cell Immunology (6 papers), Pancreatic function and diabetes (5 papers), Pancreatitis Pathology and Treatment (4 papers), Hematopoietic Stem Cell Transplantation (3 papers) and Diet, Metabolism, and Disease (3 papers). The work is most often cited by research in Epidemiology (443 citations), Hepatology (100 citations), Immunology (251 citations), Endocrinology, Diabetes and Metabolism (163 citations) and Cell Biology (94 citations). Maya Margalit has collaborated with scholars based in Israel, United States and Singapore. Frequent co-authors include Yaron Ilan, Elazar Rabbani, Dean Engelhardt, Orit Pappo, Hank Mansbach, Rohit Loomba, Juan P. Frías, Cynthia L. Hartsfield, R. Alper and Eran Elinav. Their work appears in journals such as Journal of Hepatology, Hepatology, Metabolism, Journal of Pharmacology and Experimental Therapeutics and The Journal of Pathology.

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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