Tamar Amit

156 papers receiving 8.8k citations

Peers

Tamar Amit
Comparison fields: 5 of 135
  • Biological Psychiatry 563
  • Neurology 960
  • Neurology 1.7k
  • Biochemistry 675
  • Complementary and alternative medicine 847
Replace Gerald Münch with:
Gerald Münch Australia
Silvia Mandel Israel
Kenjiro Ono Japan
Paula I. Moreira Portugal
Orly Weinreb Israel
George E. Barreto Colombia
Lawrence M. Sayre United States
Gemma Casadesús United States
Giulio Maria Pasinetti United States
Mehrdad Roghani Iran
Tamar Amit relative to Gerald Münch Australia Gerald Münch's profile →
Citations per field
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Citations per year

Countries citing papers authored by Tamar Amit

Since Specialization
Citations

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

Fields of papers citing papers by Tamar Amit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 158 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004380
2 2002330
3 2004298
4 2003297
5 2005289
6 2006241
7 2009200
8 2008176
9 2013171
10 2004165
11 2006152
12 1992148
13 2010148
14 2005145
15 2004139
16 2007139
17 2008136
18 2003131
19 2005131
20 2005127

About Tamar Amit

Tamar Amit is a scholar working on Endocrinology, Diabetes and Metabolism, Physiology, Molecular Biology, Neurology and Pharmacology, having authored 158 papers that have together received 9.0k indexed citations. Recurring topics across this work include Growth Hormone and Insulin-like Growth Factors (52 papers), Alzheimer's disease research and treatments (41 papers), Cholinesterase and Neurodegenerative Diseases (28 papers), Parkinson's Disease Mechanisms and Treatments (25 papers), Tea Polyphenols and Effects (18 papers), Nerve injury and regeneration (12 papers), Pancreatic function and diabetes (11 papers) and Tryptophan and brain disorders (11 papers). The work is most often cited by research in Biological Psychiatry (563 citations), Neurology (960 citations), Neurology (1.7k citations), Biochemistry (675 citations) and Complementary and alternative medicine (847 citations). Tamar Amit has collaborated with scholars based in Israel, United States and South Korea. Frequent co-authors include Moussa B. H. Youdim, Orly Weinreb, Silvia Mandel, Orit Bar‐Am, Lydia Reznichenko, Ze’ev Hochberg, Ronnie J. Barkey, Yona Levites, Yael Avramovich‐Tirosh and Lana Kupershmidt. Their work appears in journals such as Molecular and Cellular Endocrinology, Journal of Neurochemistry, Endocrinology, The Journal of Clinical Endocrinology & Metabolism and The FASEB Journal.

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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