Young Cha

1.7k total citations
25 papers, 772 citations indexed

About

Young Cha is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Physiology. According to data from OpenAlex, Young Cha has authored 25 papers receiving a total of 772 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 4 papers in Cellular and Molecular Neuroscience and 4 papers in Physiology. Recurrent topics in Young Cha's work include Pluripotent Stem Cells Research (14 papers), CRISPR and Genetic Engineering (9 papers) and Renal and related cancers (3 papers). Young Cha is often cited by papers focused on Pluripotent Stem Cells Research (14 papers), CRISPR and Genetic Engineering (9 papers) and Renal and related cancers (3 papers). Young Cha collaborates with scholars based in South Korea, United States and India. Young Cha's co-authors include Kyung‐Soon Park, Kwang‐Soo Kim, Pierre Leblanc, Dae‐Kwan Kim, Wongi Seol, Jin Hyuk Jung, Jihwan Song, Yongwoo Jang, Tae Yoon Park and Bin Song and has published in prestigious journals such as Nature, Nature Communications and PLoS ONE.

In The Last Decade

Young Cha

24 papers receiving 765 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Young Cha South Korea 15 473 125 121 95 93 25 772
Jack Mottahedeh United States 12 558 1.2× 108 0.9× 208 1.7× 147 1.5× 95 1.0× 14 1.1k
Gianluca Figlia Germany 15 475 1.0× 255 2.0× 127 1.0× 86 0.9× 91 1.0× 17 847
Brunella Cristofaro United Kingdom 10 434 0.9× 227 1.8× 88 0.7× 134 1.4× 62 0.7× 14 730
Pengfei Lin China 19 445 0.9× 169 1.4× 142 1.2× 97 1.0× 88 0.9× 66 872
Maria Dimitriadi United Kingdom 14 555 1.2× 59 0.5× 90 0.7× 121 1.3× 72 0.8× 17 857
Ke Gong China 16 579 1.2× 86 0.7× 241 2.0× 256 2.7× 69 0.7× 39 1.0k
Hyesook Yoon United States 19 272 0.6× 139 1.1× 116 1.0× 168 1.8× 82 0.9× 30 908
Natália Tőkési Hungary 14 479 1.0× 101 0.8× 45 0.4× 65 0.7× 114 1.2× 23 845
Ai Chen China 11 331 0.7× 53 0.4× 147 1.2× 80 0.8× 74 0.8× 20 571
Liuwang Zeng China 18 455 1.0× 85 0.7× 78 0.6× 35 0.4× 134 1.4× 27 820

Countries citing papers authored by Young Cha

Since Specialization
Citations

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

Fields of papers citing papers by Young Cha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Young Cha

This figure shows the co-authorship network connecting the top 25 collaborators of Young Cha. A scholar is included among the top collaborators of Young Cha 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 Young Cha. Young Cha 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.
Cha, Young, Pierre Leblanc, & Kwang‐Soo Kim. (2025). A new era in regenerative medicine: Cell replacement therapy for Parkinson’s disease is on the horizon. Cell stem cell. 32(6). 864–866.
2.
Cha, Young, Tae Yoon Park, Pierre Leblanc, & Kwang‐Soo Kim. (2023). Current Status and Future Perspectives on Stem Cell-Based Therapies for Parkinson’s Disease. Journal of Movement Disorders. 16(1). 22–41. 27 indexed citations
3.
Kim, Woori, Mohit Tripathi, Chun‐Hyung Kim, et al.. (2023). An optimized Nurr1 agonist provides disease-modifying effects in Parkinson’s disease models. Nature Communications. 14(1). 4283–4283. 26 indexed citations
4.
Park, Tae Yoon, Jeha Jeon, Nayeon Lee, et al.. (2023). Co-transplantation of autologous Treg cells in a cell therapy for Parkinson’s disease. Nature. 619(7970). 606–615. 76 indexed citations
5.
Cha, Young, Pierre Leblanc, Yean Ju Hong, & Kwang‐Soo Kim. (2022). Integrative analysis of mitochondrial metabolic dynamics in reprogramming human fibroblast cells. STAR Protocols. 3(2). 101401–101401. 1 indexed citations
6.
Jeon, Jeha, Bin Song, Nayeon Lee, et al.. (2022). Spotting-based differentiation of functional dopaminergic progenitors from human pluripotent stem cells. Nature Protocols. 17(3). 890–909. 14 indexed citations
7.
Cha, Young, Taewoo Kim, Jeha Jeon, et al.. (2021). SIRT2 regulates mitochondrial dynamics and reprogramming via MEK1-ERK-DRP1 and AKT1-DRP1 axes. Cell Reports. 37(13). 110155–110155. 49 indexed citations
8.
Sonntag, Kai‐Christian, Bin Song, Nayeon Lee, et al.. (2018). Pluripotent stem cell-based therapy for Parkinson’s disease: Current status and future prospects. Progress in Neurobiology. 168. 1–20. 93 indexed citations
9.
Cha, Young, Min-Joon Han, Hyuk‐Jin Cha, et al.. (2017). Metabolic control of primed human pluripotent stem cell fate and function by the miR-200c–SIRT2 axis. Nature Cell Biology. 19(5). 445–456. 136 indexed citations
10.
Heo, Sun‐Hee, Young Cha, & Kyung‐Soon Park. (2014). Hydroxyurea Induces a Hypersensitive Apoptotic Response in Mouse Embryonic Stem Cells Through p38-Dependent Acetylation of p53. Stem Cells and Development. 23(20). 2435–2442. 5 indexed citations
11.
Kim, Dae‐Kwan, et al.. (2013). Lefty1 and Lefty2 Control the Balance Between Self-Renewal and Pluripotent Differentiation of Mouse Embryonic Stem Cells. Stem Cells and Development. 23(5). 457–466. 34 indexed citations
12.
Kim, Dae‐Kwan, et al.. (2013). PI3K/Akt and Stat3 signaling regulated by PTEN control of the cancer stem cell population, proliferation and senescence in a glioblastoma cell line. International Journal of Oncology. 42(3). 921–928. 79 indexed citations
13.
Cha, Young, et al.. (2013). TCEA3 binds to TGF-beta receptor I and induces Smad-independent, JNK-dependent apoptosis in ovarian cancer cells. Cellular Signalling. 25(5). 1245–1251. 29 indexed citations
14.
Cha, Young, Sun‐Hee Heo, Bosco Seong Kyu Yang, et al.. (2013). Tcea3 Regulates the Vascular Differentiation Potential of Mouse Embryonic Stem Cells. Gene Expression. 16(1). 25–30. 10 indexed citations
15.
Cha, Young, et al.. (2012). Ell3 Enhances Differentiation of Mouse Embryonic Stem Cells by Regulating Epithelial-Mesenchymal Transition and Apoptosis. PLoS ONE. 7(6). e40293–e40293. 17 indexed citations
16.
Cha, Young. (2011). Detection and identification of human papillomavirus using a PCR-restriction fragment mass polymorphism assay. Molecular Medicine Reports. 4(4). 645–50. 2 indexed citations
17.
Cha, Young & Kyung‐Soon Park. (2010). SHP2 is a downstream target of ZAP70 to regulate JAK1/STAT3 and ERK signaling pathways in mouse embryonic stem cells. FEBS Letters. 584(19). 4241–4246. 20 indexed citations
18.
Cha, Young, Bo‐Hyun Moon, Hye-Jin Lee, et al.. (2010). Zap70 Functions to Maintain Stemness of Mouse Embryonic Stem Cells by Negatively Regulating Jak1/Stat3/c-Myc Signaling. Stem Cells. 28(9). 1476–1486. 28 indexed citations
19.
Cha, Young, et al.. (2008). Epigenetic deregulation of the human Oct4 promoter in mouse cells. Development Genes and Evolution. 218(10). 561–566. 1 indexed citations
20.
Cha, Young, Dae‐Weon Park, Suk‐Hwan Baek, et al.. (2006). Arsenic Trioxide Induces A poptosis in Human Colorectal Adenocarcinoma HT-29 Cells Through ROS. Cancer Research and Treatment. 38(1). 54–54. 13 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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