Sergey Viukov

10.9k total citations · 3 hit papers
14 papers, 4.6k citations indexed

About

Sergey Viukov is a scholar working on Molecular Biology, Genetics and Immunology. According to data from OpenAlex, Sergey Viukov has authored 14 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 4 papers in Genetics and 4 papers in Immunology. Recurrent topics in Sergey Viukov's work include CRISPR and Genetic Engineering (5 papers), Pluripotent Stem Cells Research (4 papers) and CAR-T cell therapy research (2 papers). Sergey Viukov is often cited by papers focused on CRISPR and Genetic Engineering (5 papers), Pluripotent Stem Cells Research (4 papers) and CAR-T cell therapy research (2 papers). Sergey Viukov collaborates with scholars based in Israel, United States and Austria. Sergey Viukov's co-authors include Elazar Zelzer, David Hume, Dalit Strauss‐Ayali, Martin Guilliams, Ki-Wook Kim, Steffen Jung, Michal Breker, Alexander Mildner, Bernard Malissen and Alexander V. Misharin and has published in prestigious journals such as Nature, Cell and Immunity.

In The Last Decade

Sergey Viukov

14 papers receiving 4.5k citations

Hit Papers

Fate Mapping Reveals Origins and Dynamics of Monocytes an... 2012 2026 2016 2021 2012 2014 2019 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Fate Mapping Reveals Origins and Dynamics of Monocytes and Tissue Macrophages under Homeostasis
    2012 2.3k citations Simon Yona, Ki-Wook Kim et al. Immunity profile →
  2. SOX17 Is a Critical Specifier of Human Primordial Germ Cell Fate
    2014 588 citations Naoko Irie, Leehee Weinberger et al. Cell profile →
  3. Deciphering the “m6A Code” via Antibody-Independent Quantitative Profiling
    2019 359 citations Miguel Angel García-Campos, Sarit Edelheit et al. Cell profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergey Viukov Israel 14 2.1k 1.8k 662 453 449 14 4.6k
Rupert Hallmann Germany 36 1.8k 0.8× 2.0k 1.1× 793 1.2× 347 0.8× 264 0.6× 51 5.4k
Martin Stehling Germany 32 2.3k 1.0× 1.0k 0.6× 273 0.4× 363 0.8× 373 0.8× 68 4.1k
Brenda Williams Australia 37 1.9k 0.9× 981 0.5× 310 0.5× 381 0.8× 409 0.9× 100 4.9k
Koh‐Hei Sonoda Japan 40 1.6k 0.8× 1.8k 1.0× 481 0.7× 156 0.3× 209 0.5× 267 6.6k
Marella de Bruijn United Kingdom 37 3.6k 1.7× 2.9k 1.6× 565 0.9× 264 0.6× 329 0.7× 67 7.2k
Kenji Tanigaki Japan 29 2.7k 1.3× 1.4k 0.8× 231 0.3× 294 0.6× 420 0.9× 47 4.8k
Federica Benvenuto Italy 31 1.6k 0.7× 1.5k 0.8× 475 0.7× 1.1k 2.5× 192 0.4× 64 6.1k
Keiko Funa Sweden 41 3.3k 1.6× 717 0.4× 426 0.6× 398 0.9× 488 1.1× 99 6.0k
Simón Méndez‐Ferrer United Kingdom 37 2.4k 1.1× 2.4k 1.3× 234 0.4× 749 1.7× 306 0.7× 77 8.3k
Carol A. Wise United States 35 2.7k 1.3× 1.6k 0.9× 257 0.4× 804 1.8× 1.2k 2.8× 85 5.7k

Countries citing papers authored by Sergey Viukov

Since Specialization
Citations

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

Fields of papers citing papers by Sergey Viukov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergey Viukov

This figure shows the co-authorship network connecting the top 25 collaborators of Sergey Viukov. A scholar is included among the top collaborators of Sergey Viukov 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 Sergey Viukov. Sergey Viukov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Viukov, Sergey, Tom Shani, Jonathan Bayerl, et al.. (2022). Human primed and naïve PSCs are both able to differentiate into trophoblast stem cells. Stem Cell Reports. 17(11). 2484–2500. 33 indexed citations
2.
Steinberg, Daniel, I. M. Kustanovich, Sergey Viukov, et al.. (2021). Modeling genetic epileptic encephalopathies using brain organoids. EMBO Molecular Medicine. 13(8). e13610–e13610. 34 indexed citations
3.
García-Campos, Miguel Angel, Sarit Edelheit, Ursula Toth, et al.. (2019). Deciphering the “m6A Code” via Antibody-Independent Quantitative Profiling. Cell. 178(3). 731–747.e16. 359 indexed citations breakdown →
4.
Glasner, Ariella, Assi Levi, Jonatan Enk, et al.. (2018). NKp46 Receptor-Mediated Interferon-γ Production by Natural Killer Cells Increases Fibronectin 1 to Alter Tumor Architecture and Control Metastasis. Immunity. 48(1). 107–119.e4. 173 indexed citations
5.
Glasner, Ariella, Batya Isaacson, Sergey Viukov, et al.. (2017). Increased NK cell immunity in a transgenic mouse model of NKp46 overexpression. Scientific Reports. 7(1). 13090–13090. 16 indexed citations
6.
Shwartz, Yulia, Sergey Viukov, Sharon Krief, & Elazar Zelzer. (2016). Joint Development Involves a Continuous Influx of Gdf5-Positive Cells. Cell Reports. 15(12). 2577–2587. 112 indexed citations
7.
Maza, Itay, Inbal Caspi, Asaf Zviran, et al.. (2015). Transient acquisition of pluripotency during somatic cell transdifferentiation with iPSC reprogramming factors. Nature Biotechnology. 33(7). 769–774. 101 indexed citations
8.
Irie, Naoko, Leehee Weinberger, Walfred W. C. Tang, et al.. (2014). SOX17 Is a Critical Specifier of Human Primordial Germ Cell Fate. Cell. 160(1-2). 253–268. 588 indexed citations breakdown →
9.
Yona, Simon, Ki-Wook Kim, Yochai Wolf, et al.. (2013). Fate Mapping Reveals Origins and Dynamics of Monocytes and Tissue Macrophages under Homeostasis. Immunity. 38(5). 1073–1079. 22 indexed citations
10.
Yona, Simon, Ki-Wook Kim, Yochai Wolf, et al.. (2012). Fate Mapping Reveals Origins and Dynamics of Monocytes and Tissue Macrophages under Homeostasis. Immunity. 38(1). 79–91. 2282 indexed citations breakdown →
11.
Mansour, Abed AlFatah, Ohad Gafni, Leehee Weinberger, et al.. (2012). The H3K27 demethylase Utx regulates somatic and germ cell epigenetic reprogramming. Nature. 488(7411). 409–413. 261 indexed citations
12.
Blitz, Einat, Sergey Viukov, Amnon Sharir, et al.. (2009). Bone Ridge Patterning during Musculoskeletal Assembly Is Mediated through SCX Regulation of Bmp4 at the Tendon-Skeleton Junction. Developmental Cell. 17(6). 861–873. 225 indexed citations
13.
Viukov, Sergey, Sharon Krief, Chun‐do Oh, et al.. (2009). The forming limb skeleton serves as a signaling center for limb vasculature patterning via regulation ofVegf. Development. 136(8). 1263–1272. 95 indexed citations
14.
Viukov, Sergey, et al.. (2007). HIF1α regulation ofSox9is necessary to maintain differentiation of hypoxic prechondrogenic cells during early skeletogenesis. Development. 134(21). 3917–3928. 255 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.

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