Monica Einstein

1.2k citations
14 papers · 826 indexed · h-index 10

Monica Einstein

14 papers receiving 806 citations

Peers

Monica Einstein
Comparison fields: 5 of 70
  • Endocrinology, Diabetes and Metabolism 410
  • Genetics 257
  • Pharmacology 72
  • Biochemistry 49
  • Molecular Biology 432
Replace Victor Ding with:
Victor Ding United States
Siming Liu United States
Ralph Mosley United States
Marcel Garcia France
George E. Swaneck United States
Gene F. Tutwiler United States
SHOGO ICHII Japan
Juan E. Felı́u Spain
Masaaki Kagimoto United States
Anne-Lise Pichard France
Monica Einstein relative to Victor Ding United States Victor Ding's profile →
Citations per field
00.5×3.3×
Victor Ding · 1×
Citations per year

Countries citing papers authored by Monica Einstein

Since Specialization
Citations

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

Fields of papers citing papers by Monica Einstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

14 of 14 papers shown
#Work
1 20133
2 20123
3 201148
4 200918
5 200947
6 200815
7 2008133
8 200763
9 200556
10 200417
11 20018
12
Dose-dependent and long term effects of RU58841 (androgen receptor blocker) on hair growth in the bald stumptailed Macaque.
19971
13 199617
14 1993397

About Monica Einstein

Monica Einstein is a scholar working on Biochemistry, Endocrinology, Diabetes and Metabolism and Physiology, having authored 14 papers that have together received 826 indexed citations. Recurring topics across this work include Peroxisome Proliferator-Activated Receptors (8 papers), Metabolism, Diabetes, and Cancer (5 papers), Adipose Tissue and Metabolism (5 papers), Eicosanoids and Hypertension Pharmacology (4 papers), Sexual Differentiation and Disorders (2 papers), Pancreatic function and diabetes (2 papers), Hormonal Regulation and Hypertension (2 papers) and Hormonal and reproductive studies (1 paper). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (410 citations), Genetics (257 citations) and Pharmacology (72 citations). Monica Einstein has collaborated with scholars based in United States and Japan. Frequent co-authors include Wayne M. Geissler, Hak‐Kim Chan, Siv G. E. Andersson, Keith Elliston, Lawrence Wu, Taro E. Akiyama, Joel P. Berger, Peter T. Meinke, Harold B. Wood and Ralph T. Mosley. Their work appears in journals such as Journal of Medicinal Chemistry, Bioorganic & Medicinal Chemistry Letters, Diabetes, Journal of Biological Chemistry and Journal of Investigative Dermatology.

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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