Tae‐Hoon Chung

2.2k total citations
52 papers, 1.4k citations indexed

About

Tae‐Hoon Chung is a scholar working on Molecular Biology, Hematology and Pathology and Forensic Medicine. According to data from OpenAlex, Tae‐Hoon Chung has authored 52 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 22 papers in Hematology and 14 papers in Pathology and Forensic Medicine. Recurrent topics in Tae‐Hoon Chung's work include Multiple Myeloma Research and Treatments (22 papers), Protein Degradation and Inhibitors (14 papers) and Ubiquitin and proteasome pathways (10 papers). Tae‐Hoon Chung is often cited by papers focused on Multiple Myeloma Research and Treatments (22 papers), Protein Degradation and Inhibitors (14 papers) and Ubiquitin and proteasome pathways (10 papers). Tae‐Hoon Chung collaborates with scholars based in Singapore, United States and South Korea. Tae‐Hoon Chung's co-authors include Wee Joo Chng, Rafaël Fonseca, Siok‐Bian Ng, George Mulligan, W-J Chng, Phaik Ju Teoh, Chonglei Bi, Marta Chesi, Natalia González-Paz and Seungchan Kim and has published in prestigious journals such as Journal of Clinical Investigation, Nature Communications and Blood.

In The Last Decade

Tae‐Hoon Chung

50 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tae‐Hoon Chung Singapore 22 1.0k 656 445 242 228 52 1.4k
Domenica Ronchetti Italy 23 1.2k 1.2× 578 0.9× 340 0.8× 616 2.5× 176 0.8× 54 1.6k
Owen Stephens United States 13 780 0.7× 541 0.8× 515 1.2× 197 0.8× 109 0.5× 31 1.2k
Linda Wellik United States 20 724 0.7× 471 0.7× 444 1.0× 193 0.8× 404 1.8× 34 1.3k
Karène Mahtouk France 18 831 0.8× 833 1.3× 528 1.2× 191 0.8× 90 0.4× 34 1.5k
Laurence Lodé France 16 878 0.8× 848 1.3× 429 1.0× 125 0.5× 132 0.6× 30 1.3k
Y Frutiger United States 14 857 0.8× 756 1.2× 497 1.1× 164 0.7× 183 0.8× 16 1.4k
Tammy Price-Troska United States 17 835 0.8× 637 1.0× 544 1.2× 157 0.6× 481 2.1× 34 1.6k
Sarah Waheed United States 22 1.4k 1.3× 1.7k 2.5× 935 2.1× 156 0.6× 162 0.7× 69 2.0k
Uta Bertsch Germany 15 852 0.8× 920 1.4× 577 1.3× 79 0.3× 74 0.3× 42 1.2k
Ion Cristóbal Spain 22 1.1k 1.0× 194 0.3× 430 1.0× 489 2.0× 123 0.5× 58 1.5k

Countries citing papers authored by Tae‐Hoon Chung

Since Specialization
Citations

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

Fields of papers citing papers by Tae‐Hoon Chung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae‐Hoon Chung. 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 Tae‐Hoon Chung. The network helps show where Tae‐Hoon Chung may publish in the future.

Co-authorship network of co-authors of Tae‐Hoon Chung

This figure shows the co-authorship network connecting the top 25 collaborators of Tae‐Hoon Chung. A scholar is included among the top collaborators of Tae‐Hoon Chung 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 Tae‐Hoon Chung. Tae‐Hoon Chung 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.
Zhou, Jianbiao, Tze King Tan, Tae‐Hoon Chung, et al.. (2024). Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma. Nature Communications. 15(1). 6810–6810. 3 indexed citations
2.
Chung, Tae‐Hoon, Jianbiao Zhou, Tze King Tan, et al.. (2024). The ADAR1-regulated cytoplasmic dsRNA-sensing pathway is a novel mechanism of lenalidomide resistance in multiple myeloma. Blood. 145(11). 1164–1181. 1 indexed citations
3.
Chong, Phyllis S.Y., Jing-Yuan Chooi, Phaik Ju Teoh, et al.. (2023). Histone Methyltransferase NSD2 Activates PKCα to Drive Metabolic Reprogramming and Lenalidomide Resistance in Multiple Myeloma. Cancer Research. 83(20). 3414–3427. 5 indexed citations
4.
Soekojo, Cinnie Yentia, et al.. (2022). Genomic characterization of functional high-risk multiple myeloma patients. Blood Cancer Journal. 12(1). 24–24. 25 indexed citations
5.
6.
Jia, Yunlu, Jianbiao Zhou, Tze King Tan, et al.. (2021). Myeloma-specific superenhancers affect genes of biological and clinical relevance in myeloma. Blood Cancer Journal. 11(2). 32–32. 15 indexed citations
7.
8.
Chong, Phyllis S.Y., Jianbiao Zhou, Jing-Yuan Chooi, et al.. (2019). IL6 Promotes a STAT3-PRL3 Feedforward Loop via SHP2 Repression in Multiple Myeloma. Cancer Research. 79(18). 4679–4688. 59 indexed citations
9.
Li, Boheng, Junli Yan, Tae‐Hoon Chung, et al.. (2019). MELK mediates the stability of EZH2 through site-specific phosphorylation in extranodal natural killer/T-cell lymphoma. Blood. 134(23). 2046–2058. 29 indexed citations
10.
Teoh, Phaik Ju, et al.. (2019). IL6R-STAT3-ADAR1 (P150) interplay promotes oncogenicity in multiple myeloma with 1q21 amplification. Haematologica. 105(5). 1391–1404. 43 indexed citations
11.
Chiu, Lily, et al.. (2018). Identification of novel fusion transcripts in multiple myeloma. Journal of Clinical Pathology. 71(8). 708–712. 4 indexed citations
12.
Selvarajan, Viknesvaran, Motomi Osato, Junfang Yan, et al.. (2017). RUNX3 is oncogenic in natural killer/T-cell lymphoma and is transcriptionally regulated by MYC. Leukemia. 31(10). 2219–2227. 51 indexed citations
13.
Huo, Jinhai, et al.. (2016). Adoption Of Computed Tomography Image Guidance Among Hepatocellular Carcinoma Patients With Percutaneous Radiofrequency Ablation. Value in Health. 19(3). A136–A136. 1 indexed citations
14.
Chng, Wee Joo, Tae‐Hoon Chung, Shaji Kumar, et al.. (2015). Gene signature combinations improve prognostic stratification of multiple myeloma patients. Leukemia. 30(5). 1071–1078. 38 indexed citations
15.
Al-Aidaroos, Abdul Qader, Hiu Fung Yuen, Ke Guo, et al.. (2013). Metastasis-associated PRL-3 induces EGFR activation and addiction in cancer cells. Journal of Clinical Investigation. 123(8). 3459–3471. 61 indexed citations
16.
Chung, Tae‐Hoon, George Mulligan, Rafaël Fonseca, & Wee Joo Chng. (2013). A Novel Measure of Chromosome Instability Can Account for Prognostic Difference in Multiple Myeloma. PLoS ONE. 8(6). e66361–e66361. 32 indexed citations
17.
Kim, Myung‐Chul, et al.. (2011). Metabolic syndrome is associated with increased risk of recurrent colorectal adenomas in Korean men. International Journal of Obesity. 36(7). 1007–1011. 24 indexed citations
18.
Chng, W-J, Tae‐Hoon Chung, Siok‐Bian Ng, et al.. (2011). Clinical and biological implications of MYC activation: a common difference between MGUS and newly diagnosed multiple myeloma. Leukemia. 25(6). 1026–1035. 210 indexed citations
19.
Lee, Hun-Goo, et al.. (2008). High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae. Genome Biology. 9(1). R2–R2. 7 indexed citations
20.
Chng, Wee Joo, Shaji Kumar, Scott VanWier, et al.. (2007). Molecular Dissection of Hyperdiploid Multiple Myeloma by Gene Expression Profiling. Cancer Research. 67(7). 2982–2989. 195 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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