Kwang‐Sung Ahn

841 total citations
51 papers, 655 citations indexed

About

Kwang‐Sung Ahn is a scholar working on Molecular Biology, Hematology and Oncology. According to data from OpenAlex, Kwang‐Sung Ahn has authored 51 papers receiving a total of 655 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 17 papers in Hematology and 11 papers in Oncology. Recurrent topics in Kwang‐Sung Ahn's work include Acute Myeloid Leukemia Research (10 papers), Rheumatoid Arthritis Research and Therapies (7 papers) and Chronic Myeloid Leukemia Treatments (5 papers). Kwang‐Sung Ahn is often cited by papers focused on Acute Myeloid Leukemia Research (10 papers), Rheumatoid Arthritis Research and Therapies (7 papers) and Chronic Myeloid Leukemia Treatments (5 papers). Kwang‐Sung Ahn collaborates with scholars based in South Korea, United States and Puerto Rico. Kwang‐Sung Ahn's co-authors include Eun‐Mi Koh, Hoon‐Suk Cha, Won‐Ha Lee, Eun-Kyung Bae, Ho Kim, Wonjung Kim, Chan Hong Jeon, Joong Kyong Ahn, Jaejoon Lee and Yoon‐Joong Kang and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Gastroenterology.

In The Last Decade

Kwang‐Sung Ahn

50 papers receiving 645 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kwang‐Sung Ahn South Korea 14 354 159 136 80 78 51 655
Shuming Sun China 21 566 1.6× 144 0.9× 120 0.9× 145 1.8× 35 0.4× 51 959
Eun-Kyung Bae South Korea 15 263 0.7× 162 1.0× 106 0.8× 100 1.3× 115 1.5× 28 622
Bradley J. Rabquer United States 17 362 1.0× 332 2.1× 159 1.2× 85 1.1× 177 2.3× 31 912
Laure Delavallée France 14 357 1.0× 348 2.2× 121 0.9× 60 0.8× 127 1.6× 19 798
Lu Cui China 16 345 1.0× 165 1.0× 99 0.7× 45 0.6× 17 0.2× 31 835
Yoshihiro Nishida Japan 16 384 1.1× 226 1.4× 96 0.7× 110 1.4× 49 0.6× 59 789
Chun‐Jung Ko Taiwan 18 356 1.0× 216 1.4× 152 1.1× 156 1.9× 37 0.5× 28 818
Ji‐Hyeon Ju South Korea 15 210 0.6× 317 2.0× 184 1.4× 79 1.0× 171 2.2× 23 729
Osman Çen United States 14 233 0.7× 183 1.2× 217 1.6× 88 1.1× 25 0.3× 25 637
Carl-Walter Steiner Austria 8 298 0.8× 315 2.0× 211 1.6× 96 1.2× 263 3.4× 10 775

Countries citing papers authored by Kwang‐Sung Ahn

Since Specialization
Citations

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

Fields of papers citing papers by Kwang‐Sung Ahn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kwang‐Sung Ahn. 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 Kwang‐Sung Ahn. The network helps show where Kwang‐Sung Ahn may publish in the future.

Co-authorship network of co-authors of Kwang‐Sung Ahn

This figure shows the co-authorship network connecting the top 25 collaborators of Kwang‐Sung Ahn. A scholar is included among the top collaborators of Kwang‐Sung Ahn 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 Kwang‐Sung Ahn. Kwang‐Sung Ahn 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.
Shin, Dong‐Yeop, Myung-Jin Kim, Dong Chan Kim, et al.. (2024). Elucidation of molecular basis of osteolytic bone lesions in advanced multiple myeloma. Haematologica. 109(7). 2207–2218. 1 indexed citations
2.
Lee, Mijung, Kwang‐Sung Ahn, & Manho Kim. (2024). Effects of Artemisia asiatica ex on Akkermansia muciniphila dominance for modulation of Alzheimer’s disease in mice. PLoS ONE. 19(10). e0312670–e0312670. 1 indexed citations
3.
Choi, Ji Won, Sangsoo Kim, Kwang‐Sung Ahn, et al.. (2023). RNA-Seq-Based Molecular Classification Analyses in Colorectal Cancer and Synchronous Adenoma. Cancers. 15(19). 4851–4851.
4.
Park, Soo‐Kyung, Sangsoo Kim, Chil-Woo Lee, et al.. (2023). Enrichment of Activated Fibroblasts as a Potential Biomarker for a Non-Durable Response to Anti-Tumor Necrosis Factor Therapy in Patients with Crohn’s Disease. International Journal of Molecular Sciences. 24(19). 14799–14799. 1 indexed citations
5.
Choi, Ji Won, et al.. (2023). A streamlined pipeline based on HmmUFOtu for microbial community profiling using 16S rRNA amplicon sequencing. Genomics & Informatics. 21(3). e40–e40. 2 indexed citations
6.
7.
Hwang, Kihwan, Se Jin Cho, Byung Se Choi, et al.. (2022). Fully Automated Segmentation Models of Supratentorial Meningiomas Assisted by Inclusion of Normal Brain Images. Journal of Imaging. 8(12). 327–327. 2 indexed citations
8.
Park, Soo‐Kyung, Sangsoo Kim, Chil-Woo Lee, et al.. (2021). Development of a Machine Learning Model to Distinguish between Ulcerative Colitis and Crohn’s Disease Using RNA Sequencing Data. Diagnostics. 11(12). 2365–2365. 17 indexed citations
9.
Kim, Sangsoo, et al.. (2019). Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema. BMC Ophthalmology. 19(1). 178–178. 32 indexed citations
10.
Lee, Min Kyung, Hyun Sub Cheong, Youngil Koh, et al.. (2016). Genetic Association of PARP15 Polymorphisms with Clinical Outcome of Acute Myeloid Leukemia in a Korean Population. Genetic Testing and Molecular Biomarkers. 20(11). 696–701. 14 indexed citations
11.
Koh, Youngil, et al.. (2014). Establishment of Cell Lines from Both Myeloma Bone Marrow and Plasmacytoma: SNU_MM1393_BM and SNU_MM1393_SC from a Single Patient. BioMed Research International. 2014. 1–8. 3 indexed citations
12.
Ahn, Joong Kyong, Jaejoon Lee, Eun-Kyung Bae, et al.. (2010). Increased Extracellular Survivin in the Synovial Fluid of Rheumatoid Arthritis Patients: Fibroblast-like Synoviocytes as a Potential Source of Extracellular Survivin. Inflammation. 33(6). 381–388. 38 indexed citations
13.
Cha, Hoon‐Suk, Eun-Kyung Bae, Chan Hong Jeon, et al.. (2007). Tumor necrosis factor-alpha induces vascular endothelial growth factor-C expression in rheumatoid synoviocytes.. PubMed. 34(1). 16–9. 62 indexed citations
14.
15.
Lee, Hyo‐Jeong, Yun‐Hee Rhee, Kwang‐Sung Ahn, et al.. (2006). An oriental herbal cocktail, ka-mi-kae-kyuk-tang, exerts anti-cancer activities by targeting angiogenesis, apoptosis and metastasis. Carcinogenesis. 27(12). 2455–2463. 66 indexed citations
16.
Kim, Youngju, Kwang‐Sung Ahn, Jong-Seok Lee, et al.. (2005). Improved efficacy with sequential use of histone deacetylase inhibitor, LAQ824, with common chemotherapeutic agents in head and neck squamous carcinoma cell lines. Cancer Research. 65. 1203–1203. 2 indexed citations
17.
18.
Jeon, Chan Hong, Jung‐Ho Park, Kwang‐Sung Ahn, et al.. (2003). Cytokine profiles in synovial fluid of rheumatoid arthritis.. The Korean Journal of Internal Medicine. 64(5). 576–587. 3 indexed citations
19.
Lee, Joo‐Heung, et al.. (2003). Keratin 9 gene mutations in five Korean families with epidermolytic palmoplantar keratoderma. Experimental Dermatology. 12(6). 876–881. 5 indexed citations
20.
Nath, Kamalendu, Xian Chen, Kwang‐Sung Ahn, & Shufen Chen. (2000). Characterization of the 16S rRNA gene V2 region and the operonsof. Research in Microbiology. 151(9). 747–754. 1 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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