Jae‐Yeol Joo

1.6k total citations
26 papers, 1.1k citations indexed

About

Jae‐Yeol Joo is a scholar working on Molecular Biology, Cancer Research and Cellular and Molecular Neuroscience. According to data from OpenAlex, Jae‐Yeol Joo has authored 26 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 8 papers in Cancer Research and 6 papers in Cellular and Molecular Neuroscience. Recurrent topics in Jae‐Yeol Joo's work include RNA modifications and cancer (8 papers), Cancer-related molecular mechanisms research (5 papers) and RNA Research and Splicing (5 papers). Jae‐Yeol Joo is often cited by papers focused on RNA modifications and cancer (8 papers), Cancer-related molecular mechanisms research (5 papers) and RNA Research and Splicing (5 papers). Jae‐Yeol Joo collaborates with scholars based in South Korea, United States and Japan. Jae‐Yeol Joo's co-authors include Katie Schaukowitch, Tae-Kyung Kim, Key‐Hwan Lim, Carlos Martinez, Jonathan K. Watts, Xihui Liu, Gokhul Kilaru, Sumin Yang, Lukas Farbiak and Sung‐Hyun Kim and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Neuroscience and Molecular Cell.

In The Last Decade

Jae‐Yeol Joo

25 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jae‐Yeol Joo South Korea 15 775 272 224 107 98 26 1.1k
Min Jeong Kye Germany 21 1.3k 1.7× 357 1.3× 268 1.2× 65 0.6× 90 0.9× 30 1.7k
Zane Zeier United States 17 682 0.9× 363 1.3× 91 0.4× 61 0.6× 208 2.1× 25 1.1k
Georg von Jonquières Australia 21 775 1.0× 134 0.5× 395 1.8× 54 0.5× 152 1.6× 35 1.3k
Chi Xu China 16 395 0.5× 88 0.3× 186 0.8× 92 0.9× 108 1.1× 42 828
An Liu China 14 353 0.5× 166 0.6× 184 0.8× 35 0.3× 114 1.2× 33 648
Sujata Bupp United States 8 683 0.9× 96 0.4× 321 1.4× 118 1.1× 101 1.0× 9 1.0k
Lucio Schiapparelli United States 12 519 0.7× 155 0.6× 225 1.0× 53 0.5× 40 0.4× 16 803
Felipe Baeza‐Lehnert Chile 12 606 0.8× 146 0.5× 428 1.9× 84 0.8× 52 0.5× 16 1.1k
Jocelyn Widagdo Australia 19 1.2k 1.5× 376 1.4× 353 1.6× 77 0.7× 203 2.1× 34 1.6k
Georgia Kouroupi Greece 12 358 0.5× 85 0.3× 212 0.9× 105 1.0× 62 0.6× 16 613

Countries citing papers authored by Jae‐Yeol Joo

Since Specialization
Citations

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

Fields of papers citing papers by Jae‐Yeol Joo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jae‐Yeol Joo. 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 Jae‐Yeol Joo. The network helps show where Jae‐Yeol Joo may publish in the future.

Co-authorship network of co-authors of Jae‐Yeol Joo

This figure shows the co-authorship network connecting the top 25 collaborators of Jae‐Yeol Joo. A scholar is included among the top collaborators of Jae‐Yeol Joo 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 Jae‐Yeol Joo. Jae‐Yeol Joo 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.
Yang, Sumin, Hyojung Kim, Dang-Khoa Vo, et al.. (2025). Preclinical studies and transcriptome analysis in a model of Parkinson’s disease with dopaminergic ZNF746 expression. Molecular Neurodegeneration. 20(1). 24–24. 6 indexed citations
2.
Yang, Sumin, Jieun Seo, Jeong‐Hyeon Choi, et al.. (2025). Towards understanding cancer dormancy over strategic hitching up mechanisms to technologies. Molecular Cancer. 24(1). 47–47. 3 indexed citations
3.
Kim, Sung‐Hyun, et al.. (2025). Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning‐Based Advanced Perspectives. Advanced Science. 12(15). e2413786–e2413786.
4.
5.
Yang, Sumin, et al.. (2023). Potent of strategic approaches for tauopathies ranging from single-cell transcriptome to microbiome. Animal Cells and Systems. 27(1). 378–393. 2 indexed citations
6.
Yang, Sumin, Sung‐Hyun Kim, Mingon Kang, & Jae‐Yeol Joo. (2023). Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges. Archives of Pharmacal Research. 46(6). 535–549. 6 indexed citations
7.
Kim, Sung-Hyun, Sumin Yang, Key‐Hwan Lim, et al.. (2021). Prediction of Alzheimer’s disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening. Proceedings of the National Academy of Sciences. 118(3). 18 indexed citations
8.
Joo, Jae‐Yeol, Key‐Hwan Lim, Sumin Yang, et al.. (2021). Prediction of genetic alteration of phospholipase C isozymes in brain disorders: Studies with deep learning. Advances in Biological Regulation. 82. 100833–100833. 5 indexed citations
9.
Kim, Sung‐Hyun, Key‐Hwan Lim, Sumin Yang, & Jae‐Yeol Joo. (2021). Long non-coding RNAs in brain tumors: roles and potential as therapeutic targets. Journal of Hematology & Oncology. 14(1). 77–77. 44 indexed citations
10.
Lim, Key‐Hwan, Sumin Yang, Sung-Hyun Kim, & Jae‐Yeol Joo. (2021). Identifying New COVID-19 Receptor Neuropilin-1 in Severe Alzheimer’s Disease Patients Group Brain Using Genome-Wide Association Study Approach. Frontiers in Genetics. 12. 741175–741175. 20 indexed citations
11.
Lim, Key‐Hwan, et al.. (2021). Advances in multiplex PCR for Alzheimer's disease diagnostics targeting CDK genes. Neuroscience Letters. 749. 135715–135715. 10 indexed citations
12.
Yang, Sumin, Key‐Hwan Lim, Sung‐Hyun Kim, & Jae‐Yeol Joo. (2020). Molecular landscape of long noncoding RNAs in brain disorders. Molecular Psychiatry. 26(4). 1060–1074. 47 indexed citations
13.
Lim, Key‐Hwan, Jae‐Yeol Joo, & Kwang‐Hyun Baek. (2020). The potential roles of deubiquitinating enzymes in brain diseases. Ageing Research Reviews. 61. 101088–101088. 43 indexed citations
14.
Lim, Key‐Hwan, Sumin Yang, Sung‐Hyun Kim, Sungkun Chun, & Jae‐Yeol Joo. (2020). Discoveries for Long Non-Coding RNA Dynamics in Traumatic Brain Injury. Biology. 9(12). 458–458. 12 indexed citations
15.
Schaukowitch, Katie, Austin L. Reese, Seung-Kyoon Kim, et al.. (2017). An Intrinsic Transcriptional Program Underlying Synaptic Scaling during Activity Suppression. Cell Reports. 18(6). 1512–1526. 54 indexed citations
16.
Schaukowitch, Katie, Jae‐Yeol Joo, & Tae-Kyung Kim. (2016). UV-RNA Immunoprecipitation (UV-RIP) Protocol in Neurons. Methods in molecular biology. 1468. 33–38. 8 indexed citations
17.
Joo, Jae‐Yeol, Katie Schaukowitch, Lukas Farbiak, Gokhul Kilaru, & Tae-Kyung Kim. (2015). Stimulus-specific combinatorial functionality of neuronal c-fos enhancers. Nature Neuroscience. 19(1). 75–83. 184 indexed citations
18.
Schaukowitch, Katie, Jae‐Yeol Joo, Xihui Liu, et al.. (2014). Enhancer RNA Facilitates NELF Release from Immediate Early Genes. Molecular Cell. 56(1). 29–42. 330 indexed citations
19.
Yasumura, Misato, Tomoyuki Yoshida, Sung‐Jin Lee, et al.. (2011). Glutamate receptor δ1 induces preferentially inhibitory presynaptic differentiation of cortical neurons by interacting with neurexins through cerebellin precursor protein subtypes. Journal of Neurochemistry. 121(5). 705–716. 53 indexed citations
20.
Joo, Jae‐Yeol, Sung‐Jin Lee, Takeshi Uemura, et al.. (2011). Differential interactions of cerebellin precursor protein (Cbln) subtypes and neurexin variants for synapse formation of cortical neurons. Biochemical and Biophysical Research Communications. 406(4). 627–632. 47 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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