Leonid Peshkin
Impact in
- Aging top 2%
- Biophysics top 0.5%
- Cell Image Analysis Techniques
Papers in
-
- Genomics and Phylogenetic Studies 7
- Epigenetics and DNA Methylation 6
- Single-cell and spatial transcriptomics 5
- RNA Research and Splicing 5
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- Reinforcement Learning in Robotics 7
- Co-authors
- Marc W. Kirschner (23 shared papers)Allon M. Klein (4 shared papers)Victor Li (1 shared paper)Ilke Akartuna (1 shared paper)Naren Tallapragada (1 shared paper)Linas Mažutis (1 shared paper)Adrian Veres (1 shared paper)David A. Weitz (1 shared paper)
- Journals
- Developmental Biology (4 papers)Proceedings of the National Academy of Sciences (4 papers)Cell (3 papers)Developmental Cell (2 papers)PLoS ONE (2 papers)
- Partner nations
- United StatesFranceRussia
In The Last Decade
Leonid Peshkin
65 papers receiving 4.9k citations
Leonid Peshkin's Hit Papers
Peers
Comparison fields: 5 of 159
- Aging 122
- Biophysics 367
- Molecular Biology 3.3k
- Cancer Research 551
- Immunology 412
Countries citing papers authored by Leonid Peshkin
This map shows the geographic impact of Leonid Peshkin'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 Leonid Peshkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonid Peshkin more than expected).
Fields of papers citing papers by Leonid Peshkin
This network shows the impact of papers produced by Leonid Peshkin. 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 Leonid Peshkin. The network helps show where Leonid Peshkin may publish in the future.
Co-authors
The 25 scholars most cited alongside Leonid Peshkin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 69 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells Hit paper breakdown → | 2015 | 2365 |
| 2 | The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution Hit paper breakdown → | 2018 | 377 |
| 3 | 2014 | 274 | |
| 4 | 2014 | 201 | |
| 5 | 2015 | 164 | |
| 6 | 2011 | 160 | |
| 7 | Solving very large weakly coupled Markov decision processes | 1998 | 127 |
| 8 | 2009 | 121 | |
| 9 | 2012 | 119 | |
| 10 | 2015 | 105 | |
| 11 | 2007 | 101 | |
| 12 | 2006 | 81 | |
| 13 | 2014 | 77 | |
| 14 | 2003 | 67 | |
| 15 | 2011 | 58 | |
| 16 | 2022 | 51 | |
| 17 | 2001 | 49 | |
| 18 | 2013 | 42 | |
| 19 | 2017 | 38 | |
| 20 | 2020 | 35 |
About Leonid Peshkin
Leonid Peshkin is a scholar working on Molecular Biology, Artificial Intelligence, Cell Biology, Aging and Ecology, having authored 69 papers that have together received 5.0k indexed citations. Recurring topics across this work include Genetics, Aging, and Longevity in Model Organisms (8 papers), Reinforcement Learning in Robotics (7 papers), Genomics and Phylogenetic Studies (7 papers), Epigenetics and DNA Methylation (6 papers), Advanced Proteomics Techniques and Applications (6 papers), Single-cell and spatial transcriptomics (5 papers), RNA Research and Splicing (5 papers) and Physiological and biochemical adaptations (5 papers). The work is most often cited by research in Aging (122 citations), Biophysics (367 citations), Molecular Biology (3.3k citations), Cancer Research (551 citations) and Immunology (412 citations). Leonid Peshkin has collaborated with scholars based in United States, France and Russia. Frequent co-authors include Marc W. Kirschner, Allon M. Klein, Victor Li, Ilke Akartuna, Naren Tallapragada, Linas Mažutis, Adrian Veres, David A. Weitz, Martin Wühr and Steven P. Gygi. Their work appears in journals such as Developmental Biology, Proceedings of the National Academy of Sciences, Cell, Developmental Cell and PLoS ONE.
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.