M. Lis

35 papers receiving 1.0k citations

Peers

M. Lis
Comparison fields: 5 of 75
  • Endocrine and Autonomic Systems 313
  • Cellular and Molecular Neuroscience 797
  • Behavioral Neuroscience 118
  • Reproductive Medicine 126
  • Endocrinology, Diabetes and Metabolism 192
Replace Danielle Gully with:
Danielle Gully France
Paola Lembo Canada
Ravindra K. Malhotra United States
Chantévy Pou United Kingdom
Jean‐Claude Beaujouan France
Ruta Slepetis United States
Yukio Shimomura Japan
J.A. Biggins United Kingdom
Susan E. Senogles United States
Debra Mullikin-Kilpatrick United States
M. Lis relative to Danielle Gully France Danielle Gully's profile →
Citations per field
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Citations per year

Countries citing papers authored by M. Lis

Since Specialization
Citations

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

Fields of papers citing papers by M. Lis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1977203
2 1978143
3 1976126
4 197786
5 198181
6 197978
7 197855
8 198840
9 197838
10 197738
11 199633
12 197730
13 198030
14 197826
15 197725
16 197620
17 197720
18 197418
19 200417
20 197617

About M. Lis

M. Lis is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Endocrinology, Diabetes and Metabolism, Physiology and Endocrine and Autonomic Systems, having authored 35 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neuropeptides and Animal Physiology (15 papers), Receptor Mechanisms and Signaling (12 papers), Growth Hormone and Insulin-like Growth Factors (8 papers), Regulation of Appetite and Obesity (6 papers), Adipose Tissue and Metabolism (5 papers), Chemical Synthesis and Analysis (4 papers), Cancer, Hypoxia, and Metabolism (4 papers) and Adrenal Hormones and Disorders (3 papers). The work is most often cited by research in Endocrine and Autonomic Systems (313 citations), Cellular and Molecular Neuroscience (797 citations), Behavioral Neuroscience (118 citations), Reproductive Medicine (126 citations) and Endocrinology, Diabetes and Metabolism (192 citations). M. Lis has collaborated with scholars based in Canada, United States and Belarus. Frequent co-authors include Michel Chrétien, Nabil G. Seidah, Philippe Crine, Suzanne Benjannet, Paul D. Pezalla, James E. Côté, Fernand Labrie, Françis Gossard, Rachel Leclerc and G. Pelletier. Their work appears in journals such as Proceedings of the National Academy of Sciences, Biochemical and Biophysical Research Communications, Endocrinology, FEBS Letters and Journal of Biological Chemistry.

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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