Dong-Wan Kim

4.9k total citations · 1 hit paper
52 papers, 2.9k citations indexed

About

Dong-Wan Kim is a scholar working on Oncology, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Dong-Wan Kim has authored 52 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Oncology, 10 papers in Molecular Biology and 10 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Dong-Wan Kim's work include Lung Cancer Treatments and Mutations (6 papers), Breast Cancer Treatment Studies (6 papers) and RNA modifications and cancer (5 papers). Dong-Wan Kim is often cited by papers focused on Lung Cancer Treatments and Mutations (6 papers), Breast Cancer Treatment Studies (6 papers) and RNA modifications and cancer (5 papers). Dong-Wan Kim collaborates with scholars based in South Korea, Ethiopia and Puerto Rico. Dong-Wan Kim's co-authors include Hyeshik Chang, V. Narry Kim, Jeong-Sun Yang, Joo-Yeon Lee, Jun Won Kim, Dae Seog Heo, Seungmin Lee, Yung‐Jue Bang, Tae‐You Kim and Seock‐Ah Im and has published in prestigious journals such as Science, Cell and Cell stem cell.

In The Last Decade

Dong-Wan Kim

48 papers receiving 2.8k citations

Hit Papers

The Architecture of SARS-... 2020 2026 2022 2024 2020 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The Architecture of SARS-CoV-2 Transcriptome
    2020 1.4k citations Dong-Wan Kim, V. Narry Kim et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dong-Wan Kim South Korea 21 1.1k 884 499 452 236 52 2.9k
Jun Won Kim South Korea 25 999 0.9× 781 0.9× 438 0.9× 388 0.9× 499 2.1× 138 3.1k
Sarah Wheeler United States 25 783 0.7× 935 1.1× 977 2.0× 362 0.8× 298 1.3× 98 3.4k
Pisit Tangkijvanich Thailand 35 460 0.4× 1.6k 1.8× 438 0.9× 634 1.4× 242 1.0× 265 5.1k
Mohamed Abdel‐Hamid Egypt 38 317 0.3× 998 1.1× 451 0.9× 177 0.4× 130 0.6× 127 4.1k
Pei Liu China 24 835 0.7× 559 0.6× 259 0.5× 186 0.4× 236 1.0× 149 3.1k
Yongxiang Yi China 20 1.7k 1.5× 579 0.7× 298 0.6× 330 0.7× 179 0.8× 49 2.8k
Jialu Li China 28 1.2k 1.1× 1.9k 2.2× 378 0.8× 1.5k 3.3× 177 0.8× 92 4.0k

Countries citing papers authored by Dong-Wan Kim

Since Specialization
Citations

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

Fields of papers citing papers by Dong-Wan Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong-Wan Kim. 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 Dong-Wan Kim. The network helps show where Dong-Wan Kim may publish in the future.

Co-authorship network of co-authors of Dong-Wan Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Dong-Wan Kim. A scholar is included among the top collaborators of Dong-Wan Kim 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 Dong-Wan Kim. Dong-Wan Kim 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.
Huang, Tao, Ayaka Yanagida, Dong-Wan Kim, et al.. (2025). Inhibition of PRC2 enables self-renewal of blastoid-competent naive pluripotent stem cells from chimpanzee. Cell stem cell. 32(4). 627–639.e8. 1 indexed citations
2.
Seo, Seonguk, Dong-Wan Kim, & Bohyung Han. (2025). Revisiting Machine Unlearning with Dimensional Alignment. 3206–3215.
3.
Youk, Jeonghwan, Yoojoo Lim, Hwang‐Phill Kim, et al.. (2024). Analytical and Clinical Validation of a Highly Sensitive NGS-Based ctDNA Assay with Real-World Concordance in Non–Small Cell Lung Cancer. Cancer Research and Treatment. 56(3). 765–773. 5 indexed citations
4.
5.
Kim, Dong-Wan, et al.. (2022). Analyzing viral epitranscriptomes using nanopore direct RNA sequencing. The Journal of Microbiology. 60(9). 867–876. 7 indexed citations
6.
Kim, Dong-Wan, et al.. (2021). Impact of Electronic Aid to Daily Living (EADL) on Activities of Daily Living: A Systematic Review. The Journal of Korean Society of Occupational Therapy. 29(3). 55–68. 2 indexed citations
7.
Won, Joungha, Myungsun Park, Tai Young Kim, et al.. (2020). Development of a Laboratory-safe and Low-cost Detection Protocol for SARS-CoV-2 of the Coronavirus Disease 2019 (COVID-19). Experimental Neurobiology. 29(5). 402–402. 14 indexed citations
8.
Kim, Dong-Wan, Young-Suk Lee, Soo Jin Jung, et al.. (2020). Viral hijacking of the TENT4–ZCCHC14 complex protects viral RNAs via mixed tailing. Nature Structural & Molecular Biology. 27(6). 581–588. 47 indexed citations
9.
Lee, Seungmin, Dong-Wan Kim, Namil Kim, & Seong-Gyun Jeong. (2019). Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation. 91–100. 102 indexed citations
10.
Lim, Jaechul, Dong-Wan Kim, Young-Suk Lee, et al.. (2018). Mixed tailing by TENT4A and TENT4B shields mRNA from rapid deadenylation. Science. 361(6403). 701–704. 104 indexed citations
11.
Park, Jong Sun, Dong-Wan Kim, Jinwoo Lee, et al.. (2011). Adverse pulmonary reactions associated with the use of monoclonal antibodies in cancer patients. Respiratory Medicine. 106(3). 443–450. 22 indexed citations
12.
13.
Keam, Bhumsuk, Seock‐Ah Im, Sohee Park, et al.. (2011). Nomogram predicting clinical outcomes in breast cancer patients treated with neoadjuvant chemotherapy. Journal of Cancer Research and Clinical Oncology. 137(9). 1301–1308. 31 indexed citations
14.
Wu, Hong‐Gyun, Charn Il Park, Dae Seog Heo, et al.. (2010). Definitive Radiotherapy With or Without Chemotherapy for T3-4N0 Squamous Cell Carcinoma of the Maxillary Sinus and Nasal Cavity. Japanese Journal of Clinical Oncology. 40(6). 542–548. 23 indexed citations
15.
Kim, Hee Jun, Yu Jung Kim, Yu Jung Kim, et al.. (2009). Patterns of palliative procedures and clinical outcomes in patients with advanced non-small cell lung cancer. Lung Cancer. 65(2). 242–246. 11 indexed citations
16.
Kim, Yu Jung, Min A Kim, Seock‐Ah Im, et al.. (2008). Metastasis-Associated Protein S100A4 and p53 Predict Relapse in Curatively Resected Stage III and IV (M0) Gastric Cancer. Cancer Investigation. 26(2). 152–158. 24 indexed citations
17.
18.
Oh, Do‐Youn, Jee-Hyun Kim, Dong-Wan Kim, et al.. (2005). CPR or DNR? End-of-life decision in Korean cancer patients: a single center’s experience. Supportive Care in Cancer. 14(2). 103–108. 70 indexed citations
19.
Choi, Paul C.L., et al.. (2003). A Case of Gastric Submucosal Tumor Suspected to be Caused by Anisakis. Clinical Endoscopy. 27(1). 26–30. 1 indexed citations
20.
Kim, Dong-Wan, Yung‐Jue Bang, Dae Seog Heo, & Noe Kyeong Kim. (2002). Colorectal Cancer in Korea: Characteristics and Trends. Tumori Journal. 88(4). 262–265. 8 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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