Stephen S. Chung

6.5k total citations
90 papers, 2.2k citations indexed

About

Stephen S. Chung is a scholar working on Molecular Biology, Hematology and Cell Biology. According to data from OpenAlex, Stephen S. Chung has authored 90 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 39 papers in Hematology and 22 papers in Cell Biology. Recurrent topics in Stephen S. Chung's work include Acute Myeloid Leukemia Research (36 papers), Aldose Reductase and Taurine (20 papers) and Hematopoietic Stem Cell Transplantation (11 papers). Stephen S. Chung is often cited by papers focused on Acute Myeloid Leukemia Research (36 papers), Aldose Reductase and Taurine (20 papers) and Hematopoietic Stem Cell Transplantation (11 papers). Stephen S. Chung collaborates with scholars based in United States, Hong Kong and China. Stephen S. Chung's co-authors include Ben C.B. Ko, Deliang Cao, Sheung Tat Fan, Christopher Y. Park, Sookja Kim Chung, Sookja Kim Chung, Kenneth H. Gabbay, Kurt M. Bohren, Karen S.L. Lam and Wenhuo Hu and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Stephen S. Chung

79 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stephen S. Chung United States 27 1.1k 944 382 324 246 90 2.2k
Steven W. Paugh United States 18 2.1k 2.0× 708 0.8× 101 0.3× 346 1.1× 125 0.5× 26 2.6k
Hyug Moo Kwon South Korea 32 1.1k 1.0× 964 1.0× 295 0.8× 365 1.1× 28 0.1× 85 2.4k
Katsuhiko Asanuma Japan 29 1.5k 1.4× 361 0.4× 120 0.3× 284 0.9× 91 0.4× 89 4.0k
Kimihiko Sano Japan 24 1.3k 1.2× 290 0.3× 165 0.4× 231 0.7× 321 1.3× 60 2.7k
Reid Huber United States 19 1.5k 1.4× 354 0.4× 202 0.5× 671 2.1× 141 0.6× 50 3.5k
Thomas C. Markello United States 28 943 0.9× 174 0.2× 428 1.1× 173 0.5× 118 0.5× 68 2.5k
Terttu Suormala Switzerland 30 1.5k 1.4× 758 0.8× 273 0.7× 207 0.6× 146 0.6× 81 2.7k
Lin Zuo China 19 1.4k 1.3× 238 0.3× 150 0.4× 265 0.8× 138 0.6× 42 3.0k
Christine Leroy France 23 738 0.7× 195 0.2× 120 0.3× 229 0.7× 231 0.9× 47 1.8k
Grisha Pirianov United Kingdom 23 2.1k 2.0× 615 0.7× 73 0.2× 317 1.0× 65 0.3× 39 3.2k

Countries citing papers authored by Stephen S. Chung

Since Specialization
Citations

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

Fields of papers citing papers by Stephen S. Chung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen S. 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 Stephen S. Chung. The network helps show where Stephen S. Chung may publish in the future.

Co-authorship network of co-authors of Stephen S. Chung

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen S. Chung. A scholar is included among the top collaborators of Stephen S. 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 Stephen S. Chung. Stephen S. 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.
McConville, Michael, et al.. (2025). A germline ETV6 mutation disrupts hematopoiesis via de novo creation of a nuclear export signal. Science Advances. 11(16). eadu4058–eadu4058.
2.
McCoy, Melissa K., Milo M. Lin, Cheng Cheng Zhang, et al.. (2025). Synergistic and antagonistic drug interactions are prevalent but not conserved across acute myeloid leukemia cell lines. Scientific Reports. 15(1). 19431–19431. 1 indexed citations
3.
Kroger, Benjamin, Roni Tamari, Carmelo Gurnari, et al.. (2024). Prediction of Post-Transplant Relapse of the Myelodysplastic Syndromes Via Evaluation of Stem Cells. Blood. 144(Supplement 1). 777–777.
4.
McConville, Michael, et al.. (2023). Familial Thrombocytopenia-Associated Germline ETV6 P214L Mutation Results in XPO1-Mediated Nuclear Export. Blood. 142(Supplement 1). 1298–1298. 1 indexed citations
5.
Zahid, Mohammad Faizan, Olga K. Weinberg, Robert H. Collins, et al.. (2023). Identifying patients at risk for hereditary myeloid malignancy syndromes incorporating a novel, self‐administered questionnaire to an initial screening platform. European Journal Of Haematology. 111(6). 844–850. 1 indexed citations
6.
Kroger, Benjamin, Umar Khan, Robert H. Collins, et al.. (2023). Abstract 5925: Prevalence of clonal hematopoiesis in patients with monoclonal gammopathy of undetermined significance. Cancer Research. 83(7_Supplement). 5925–5925. 1 indexed citations
7.
Khan, Adeel, Urvashi Pandey, Fieke W. Hoff, et al.. (2023). Shattering myths regarding Hispanic ethnicity and survival in acute myeloid leukemia: Insights from the National Cancer Database.. Journal of Clinical Oncology. 41(16_suppl). 7032–7032.
8.
Al‐Kali, Aref, Dragan Jevremović, Phuong L. Nguyen, et al.. (2023). IPSS-M - Use for Predicting Survival and Progression in Patients with Ccus - a Retrospective Multi-Institutional Study. Blood. 142(Supplement 1). 4590–4590. 2 indexed citations
9.
Yao, Huiyu, Yue Ma, Yuannyu Zhang, et al.. (2022). Epo-IGF1R cross talk expands stress-specific progenitors in regenerative erythropoiesis and myeloproliferative neoplasm. Blood. 140(22). 2371–2384. 8 indexed citations
10.
Özer, Muhammet, Rong Wang, Prapti A. Patel, et al.. (2022). The impact of race and ethnicity on outcomes of patients with myelodysplastic syndromes: a population-based analysis. Leukemia & lymphoma. 63(7). 1651–1659. 4 indexed citations
11.
Martin, Gaëlle, Nainita Roy, Sohini Chakraborty, et al.. (2019). CD97 is a critical regulator of acute myeloid leukemia stem cell function. The Journal of Experimental Medicine. 216(10). 2362–2377. 27 indexed citations
12.
Lohse, Ines, Keira Pereira, Stephen S. Chung, et al.. (2015). Co-Injection of patient-derived fibroblasts enhances tumor growth, stromal invasion, and epithelial-to-mesenchymal transition. 4(1). 2–2. 2 indexed citations
13.
Chung, Stephen S. & Christopher Y. Park. (2014). MicroRNA Dysregulation in the Myelodysplastic Syndromes. MicroRNA. 2(3). 174–186. 1 indexed citations
14.
Liu, Ziwen, Linlin Zhong, Paulette A. Krishack, et al.. (2009). Structure and promoter characterization of aldo–keto reductase family 1 B10 gene. Gene. 437(1-2). 39–44. 26 indexed citations
15.
16.
Ko, Ben C.B., James Y. Yang, Zhirong Jiang, et al.. (2005). Transgenic Mice Expressing Dominant-negative Osmotic-response Element-binding Protein (OREBP) in Lens Exhibit Fiber Cell Elongation Defect Associated with Increased DNA Breaks. Journal of Biological Chemistry. 280(20). 19986–19991. 26 indexed citations
17.
Leung, Justin, et al.. (2004). Endothelial Cell-specific Over-expression of Endothelin- 1 Leads to More Severe Cerebral Damage following Transient Middle Cerebral Artery Occlusion. Journal of Cardiovascular Pharmacology. 44(Supplement 1). S293–S300. 31 indexed citations
18.
19.
Lo, Acy, et al.. (2001). Endothelin‐1 protects astrocytes from hypoxic/ischemic injury. The FASEB Journal. 15(3). 618–626. 48 indexed citations
20.
Prakash‐Cheng, Ainu, et al.. (1993). The expression and regulation of hsd K genes after conjugative transfer. Molecular and General Genetics MGG. 241-241(5-6). 491–496. 31 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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