Jason Yu

2.5k total citations · 2 hit papers
21 papers, 1.8k citations indexed

About

Jason Yu is a scholar working on Molecular Biology, Biomedical Engineering and Physiology. According to data from OpenAlex, Jason Yu has authored 21 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 4 papers in Biomedical Engineering and 2 papers in Physiology. Recurrent topics in Jason Yu's work include CRISPR and Genetic Engineering (8 papers), Pluripotent Stem Cells Research (7 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Jason Yu is often cited by papers focused on CRISPR and Genetic Engineering (8 papers), Pluripotent Stem Cells Research (7 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Jason Yu collaborates with scholars based in United Kingdom, Germany and United States. Jason Yu's co-authors include Wei Cui, Kosuke Yusa, Markus Ralser, Michael Mülleder, Thamil Selvee Ramasamy, Clara Correia‐Melo, Vadim Demichev, Anja Freiwald, Christoph B. Messner and Lucía Herrera-Domínguez and has published in prestigious journals such as Nature, Cell and Nucleic Acids Research.

In The Last Decade

Jason Yu

20 papers receiving 1.8k citations

Hit Papers

Proliferation, survival and metabolism: the role of PI3K/... 2016 2026 2019 2022 2016 2021 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jason Yu United Kingdom 15 1.1k 198 157 155 138 21 1.8k
Judith Jans Netherlands 27 1.3k 1.1× 263 1.3× 107 0.7× 129 0.8× 243 1.8× 100 2.1k
Ida Chiara Guerrera France 26 981 0.9× 139 0.7× 116 0.7× 78 0.5× 147 1.1× 96 1.8k
Christoph Krisp Germany 23 736 0.6× 132 0.7× 89 0.6× 94 0.6× 97 0.7× 60 1.4k
Xuyang Zhao China 27 1.3k 1.1× 397 2.0× 192 1.2× 274 1.8× 95 0.7× 105 2.2k
Spyros I. Vernardis United Kingdom 9 1.1k 1.0× 119 0.6× 96 0.6× 163 1.1× 115 0.8× 11 1.8k
Lingling Liu China 24 1.1k 1.0× 376 1.9× 161 1.0× 202 1.3× 224 1.6× 105 2.0k
Tanxi Cai China 22 960 0.8× 256 1.3× 61 0.4× 86 0.6× 95 0.7× 39 1.4k
Samuel G. Mackintosh United States 30 2.2k 1.9× 210 1.1× 230 1.5× 290 1.9× 203 1.5× 79 2.9k
Sung Goo Park South Korea 24 939 0.8× 160 0.8× 129 0.8× 153 1.0× 138 1.0× 63 1.4k
Yung Joon Yoo South Korea 23 1.5k 1.3× 214 1.1× 175 1.1× 310 2.0× 128 0.9× 54 2.2k

Countries citing papers authored by Jason Yu

Since Specialization
Citations

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

Fields of papers citing papers by Jason Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jason Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Jason Yu. A scholar is included among the top collaborators of Jason Yu 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 Jason Yu. Jason Yu 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.
Correia‐Melo, Clara, Stephan Kamrad, Roland Tengölics, et al.. (2023). Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan. Cell. 186(1). 63–79.e21. 32 indexed citations
2.
Zhang, Hong, Jason Yu, Sukyeong Lee, et al.. (2023). Coordination between aminoacylation and editing to protect against proteotoxicity. Nucleic Acids Research. 51(19). 10606–10618. 4 indexed citations
3.
Yu, Jason, et al.. (2023). Monitoring Drug Substance Drying with Online GC. Organic Process Research & Development. 27(11). 2067–2071.
4.
Yu, Jason, Benjamin M. Heineike, Johannes Hartl, et al.. (2022). Inorganic sulfur fixation via a new homocysteine synthase allows yeast cells to cooperatively compensate for methionine auxotrophy. PLoS Biology. 20(12). e3001912–e3001912. 6 indexed citations
5.
Yu, Jason, Clara Correia‐Melo, Francisco Zorrilla, et al.. (2022). Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance. Nature Microbiology. 7(4). 542–555. 74 indexed citations
6.
Yu, Jason, et al.. (2022). Development of a multi-container extrusion method for extruding lightweight wide plates and sheets. IOP Conference Series Materials Science and Engineering. 1270(1). 12061–12061. 1 indexed citations
7.
Messner, Christoph B., Vadim Demichev, Nic Bloomfield, et al.. (2021). Ultra-fast proteomics with Scanning SWATH. Nature Biotechnology. 39(7). 846–854. 179 indexed citations breakdown →
8.
Vowinckel, Jakob, Johannes Hartl, Hans Marx, et al.. (2021). The metabolic growth limitations of petite cells lacking the mitochondrial genome. Nature Metabolism. 3(11). 1521–1535. 27 indexed citations
9.
Gu, Muxin, Étienne De Braekeleer, Malgorzata Gozdecka, et al.. (2020). KAT7 is a genetic vulnerability of acute myeloid leukemias driven by MLL rearrangements. Leukemia. 35(4). 1012–1022. 33 indexed citations
10.
Yu, Jason & Kosuke Yusa. (2019). Genome-wide CRISPR-Cas9 screening in mammalian cells. Methods. 164-165. 29–35. 50 indexed citations
11.
Olín‐Sandoval, Viridiana, Jason Yu, Leonor Miller‐Fleming, et al.. (2019). Lysine harvesting is an antioxidant strategy and triggers underground polyamine metabolism. Nature. 572(7768). 249–253. 120 indexed citations
12.
Li, Meng, Jason Yu, Katarzyna Tilgner, et al.. (2018). Genome-wide CRISPR-KO Screen Uncovers mTORC1-Mediated Gsk3 Regulation in Naive Pluripotency Maintenance and Dissolution. Cell Reports. 24(2). 489–502. 57 indexed citations
13.
Zou, Junrong, Tingting Lei, Jason Yu, et al.. (2018). Mechanisms shaping the role of ERK1/2 in cellular sene scence (Review). Molecular Medicine Reports. 19(2). 759–770. 93 indexed citations
14.
Yu, Jason & Wei Cui. (2016). Proliferation, survival and metabolism: the role of PI3K/AKT/mTOR signalling in pluripotency and cell fate determination. Development. 143(17). 3050–3060. 853 indexed citations breakdown →
15.
Yu, Jason, Thamil Selvee Ramasamy, Nick Murphy, et al.. (2015). PI3K/mTORC2 regulates TGF-β/Activin signalling by modulating Smad2/3 activity via linker phosphorylation. Nature Communications. 6(1). 7212–7212. 76 indexed citations
16.
Yu, Chengbo, Guoquan Lv, Hongcui Cao, et al.. (2014). Rapid Large-Scale Culturing of Microencapsulated Hepatocytes: A Promising Approach for Cell-Based Hepatic Support. Transplantation Proceedings. 46(5). 1649–1657. 7 indexed citations
17.
Yu, Jason, et al.. (2014). TRF2-Mediated Stabilization of hREST4 Is Critical for the Differentiation and Maintenance of Neural Progenitors. Stem Cells. 32(8). 2111–2122. 29 indexed citations
18.
19.
Noisa, Parinya, et al.. (2012). Identification and Characterisation of the Early Differentiating Cells in Neural Differentiation of Human Embryonic Stem Cells. PLoS ONE. 7(5). e37129–e37129. 32 indexed citations
20.
Yu, Jason, Maria da Glória S. Carvalho, Bernard Beall, & Moon H. Nahm. (2008). A rapid pneumococcal serotyping system based on monoclonal antibodies and PCR. Journal of Medical Microbiology. 57(2). 171–178. 43 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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