Leonid Peshkin

25.1k total citations · 2 hit papers
69 papers, 4.9k citations indexed

About

Leonid Peshkin is a scholar working on Molecular Biology, Artificial Intelligence and Cell Biology. According to data from OpenAlex, Leonid Peshkin has authored 69 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 15 papers in Artificial Intelligence and 8 papers in Cell Biology. Recurrent topics in Leonid Peshkin's work include Genetics, Aging, and Longevity in Model Organisms (8 papers), Genomics and Phylogenetic Studies (8 papers) and Reinforcement Learning in Robotics (7 papers). Leonid Peshkin is often cited by papers focused on Genetics, Aging, and Longevity in Model Organisms (8 papers), Genomics and Phylogenetic Studies (8 papers) and Reinforcement Learning in Robotics (7 papers). Leonid Peshkin collaborates with scholars based in United States, Russia and France. Leonid Peshkin's co-authors include Marc W. Kirschner, Allon M. Klein, Ilke Akartuna, Adrian Veres, Victor Li, Linas Mažutis, Naren Tallapragada, David A. Weitz, Martin Wühr and Steven P. Gygi and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Leonid Peshkin

63 papers receiving 4.8k citations

Hit Papers

Droplet Barcoding for Single-Cell Transcriptomics Applied... 2015 2026 2018 2022 2015 2018 500 1000 1.5k 2.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Leonid Peshkin United States 24 3.4k 646 601 437 425 69 4.9k
Omar D. Perez United States 22 2.2k 0.6× 377 0.6× 255 0.4× 743 1.7× 321 0.8× 28 3.7k
Vivien Marx United States 26 2.0k 0.6× 553 0.9× 309 0.5× 191 0.4× 217 0.5× 180 3.7k
Olaf Wolkenhauer Germany 41 3.7k 1.1× 341 0.5× 1.0k 1.7× 269 0.6× 258 0.6× 277 5.6k
Colin Clarke Ireland 27 3.4k 1.0× 509 0.8× 485 0.8× 434 1.0× 248 0.6× 64 5.9k
Kedar Nath Natarajan Denmark 18 3.3k 1.0× 287 0.4× 792 1.3× 593 1.4× 143 0.3× 27 4.5k
Karen Sachs United States 12 3.1k 0.9× 517 0.8× 296 0.5× 1.3k 2.9× 381 0.9× 28 4.7k
Nikolay Samusik United States 19 3.0k 0.9× 308 0.5× 548 0.9× 971 2.2× 116 0.3× 32 4.3k
Markus W. Covert United States 38 5.6k 1.6× 1.3k 2.0× 535 0.9× 948 2.2× 209 0.5× 74 8.0k
Florian Buettner Germany 26 3.6k 1.1× 291 0.5× 697 1.2× 855 2.0× 158 0.4× 52 5.3k
Diego di Bernardo Italy 41 5.3k 1.5× 301 0.5× 487 0.8× 294 0.7× 213 0.5× 143 7.2k

Countries citing papers authored by Leonid Peshkin

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Leonid Peshkin

This figure shows the co-authorship network connecting the top 25 collaborators of Leonid Peshkin. A scholar is included among the top collaborators of Leonid Peshkin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Leonid Peshkin. Leonid Peshkin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
François-Campion, Valentin, François Berger, Mami Oikawa, et al.. (2025). Sperm derived H2AK119ub1 is required for embryonic development in Xenopus laevis. Nature Communications. 16(1). 3268–3268.
2.
Itallie, Elizabeth Van, Matthew Sonnett, Marian Kalocsay, et al.. (2025). Transitions in the proteome and phospho-proteome during Xenopus laevis development. Developmental Biology. 525. 155–171. 1 indexed citations
4.
Peshkin, Leonid, Enrico Maria Daldello, Elizabeth Van Itallie, et al.. (2025). Decoding protein phosphorylation during oocyte meiotic divisions using phosphoproteomics. eLife. 13.
5.
Mukherjee, Avik, Yanqing Huang, Jens Elgeti, et al.. (2025). Membrane potential mediates the cellular response to mechanical pressure. Cell. 189(1). 143–160.e22.
6.
Petrova, Kseniya, et al.. (2024). A new atlas to study embryonic cell types in Xenopus. Developmental Biology. 511. 76–83. 1 indexed citations
7.
Kappès, Vincent, et al.. (2024). A time-resolved single-cell roadmap of the logic driving anterior neural crest diversification from neural border to migration stages. Proceedings of the National Academy of Sciences. 121(19). e2311685121–e2311685121. 8 indexed citations
8.
Peshkin, Leonid, et al.. (2023). Open Genes—a new comprehensive database of human genes associated with aging and longevity. Nucleic Acids Research. 52(D1). D950–D962. 11 indexed citations
9.
Morselli, Marco, et al.. (2023). Age-associated DNA methylation changes in Xenopus frogs. Epigenetics. 18(1). 2201517–2201517. 5 indexed citations
10.
Gorbsky, Gary J., John R. Daum, Hitoshi Yoshida, et al.. (2022). Developing immortal cell lines from Xenopus embryos , four novel cell lines derived from Xenopus tropicalis. Open Biology. 12(7). 220089–220089. 2 indexed citations
11.
Popadin, Konstantin, Konstantin Gunbin, Leonid Peshkin, et al.. (2022). Mitochondrial Pseudogenes Suggest Repeated Inter-Species Hybridization among Direct Human Ancestors. Genes. 13(5). 810–810. 9 indexed citations
12.
Shindyapina, Anastasia V., Yongmin Cho, Alaattin Kaya, et al.. (2022). Rapamycin treatment during development extends life span and health span of male mice and Daphnia magna. Science Advances. 8(37). eabo5482–eabo5482. 48 indexed citations
13.
Cho, Yongmin, et al.. (2022). Intelligent high‐throughput intervention testing platform in Daphnia. Aging Cell. 21(3). e13571–e13571. 14 indexed citations
14.
Liu, Xili, Seungeun Oh, Leonid Peshkin, & Marc W. Kirschner. (2020). Computationally enhanced quantitative phase microscopy reveals autonomous oscillations in mammalian cell growth. Proceedings of the National Academy of Sciences. 117(44). 27388–27399. 34 indexed citations
15.
Peshkin, Leonid & Marc W. Kirschner. (2020). A cell type annotation Jamboree—Revival of а communal science forum. genesis. 58(9). e23383–e23383. 3 indexed citations
16.
Kalocsay, Marian, et al.. (2020). YAP regulates cell size and growth dynamics via non-cell autonomous mediators. eLife. 9. 35 indexed citations
17.
Peshkin, Leonid, Meera Gupta, Lillia V. Ryazanova, & Martin Wühr. (2019). Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification Concordance*[S]. Molecular & Cellular Proteomics. 18(10). 2108–2120. 19 indexed citations
18.
Briggs, James, Caleb Weinreb, Daniel E. Wagner, et al.. (2018). The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science. 360(6392). 365 indexed citations breakdown →
19.
Presler, Marc, Elizabeth Van Itallie, Allon M. Klein, et al.. (2017). Proteomics of phosphorylation and protein dynamics during fertilization and meiotic exit in the Xenopus egg. Proceedings of the National Academy of Sciences. 114(50). E10838–E10847. 38 indexed citations
20.
Meuleau, Nicolas, Miloš Hauskrecht, Kee-Eung Kim, et al.. (1998). Solving very large weakly coupled Markov decision processes. National Conference on Artificial Intelligence. 165–172. 126 indexed citations

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026