Sun Kim

4.2k total citations · 1 hit paper
173 papers, 2.7k citations indexed

About

Sun Kim is a scholar working on Molecular Biology, Cancer Research and Plant Science. According to data from OpenAlex, Sun Kim has authored 173 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 110 papers in Molecular Biology, 25 papers in Cancer Research and 22 papers in Plant Science. Recurrent topics in Sun Kim's work include Bioinformatics and Genomic Networks (46 papers), Gene expression and cancer classification (31 papers) and Computational Drug Discovery Methods (18 papers) Sun Kim is often cited by papers focused on Bioinformatics and Genomic Networks (46 papers), Gene expression and cancer classification (31 papers) and Computational Drug Discovery Methods (18 papers) Sun Kim collaborates with scholars based in South Korea, United States and Puerto Rico Sun Kim's co-authors include Sangseon Lee, Sangsoo Lim, Heejoon Chae, Youngjun Park, Kenneth P. Nephew, Inuk Jung, Kyuri Jo, Seungyoon Nam, Minsik Oh and Seokjun Seo and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Sun Kim

166 papers receiving 2.6k citations

Hit Papers

SARS-CoV-2 and Stroke in a New York Healthcare System 2020 2026 2022 2024 2020 100 200 300 400
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. SARS-CoV-2 and Stroke in a New York Healthcare System
    2020 442 citations Shadi Yaghi, Koto Ishida et al. Stroke profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sun Kim South Korea 27 1.4k 423 382 335 287 173 2.7k
Yanan Cao China 31 1.4k 1.0× 558 1.3× 358 0.9× 552 1.6× 424 1.5× 151 3.5k
Hsueh‐Fen Juan Taiwan 40 3.2k 2.3× 1.1k 2.6× 259 0.7× 333 1.0× 507 1.8× 165 4.8k
Rob M. Ewing United Kingdom 26 1.8k 1.3× 164 0.4× 418 1.1× 230 0.7× 233 0.8× 67 3.1k
Yunping Zhu China 28 2.5k 1.8× 464 1.1× 75 0.2× 208 0.6× 451 1.6× 112 4.1k
Mikaela Koutrouli Denmark 5 2.2k 1.6× 463 1.1× 84 0.2× 99 0.3× 303 1.1× 12 3.9k
Farrokh Mehryary Finland 8 2.1k 1.5× 460 1.1× 84 0.2× 94 0.3× 299 1.0× 15 3.8k
Keunsoo Kang South Korea 31 2.2k 1.6× 640 1.5× 67 0.2× 337 1.0× 618 2.2× 149 3.7k
Davood Bashash Iran 31 1.6k 1.2× 457 1.1× 167 0.4× 388 1.2× 659 2.3× 200 3.6k
Mickaël Guedj France 23 2.2k 1.6× 413 1.0× 90 0.2× 72 0.2× 342 1.2× 54 4.0k
Jun Zhu United States 42 3.5k 2.5× 908 2.1× 165 0.4× 194 0.6× 761 2.7× 146 5.5k

Countries citing papers authored by Sun Kim

Since Specialization
Citations

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

Fields of papers citing papers by Sun Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sun Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Sun Kim. A scholar is included among the top collaborators of Sun 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 Sun Kim. Sun 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.
Lee, Sungwon, et al.. (2025). Human cytomegalovirus long non-coding RNA counteracts nuclear cGAS to facilitate immune evasion. Nature Microbiology. 10(9). 2275–2290. 1 indexed citations
2.
Koo, Bonil, Seokhyeon Kim, Ja Min Byun, et al.. (2025). Increased local DNA methylation disorder in AMLs with DNMT3A-destabilizing variants and its clinical implication. Nature Communications. 16(1). 560–560. 5 indexed citations
3.
4.
Yoon, Jung-Ki, Kyoung‐Hee Lee, Dabin Jeong, et al.. (2023). Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure. Journal of Korean Medical Science. 38(29). e220–e220. 5 indexed citations
5.
Lee, Sangseon, et al.. (2023). Exploring chemical space for lead identification by propagating on chemical similarity network. Computational and Structural Biotechnology Journal. 21. 4187–4195. 4 indexed citations
6.
Na, Byunggook, et al.. (2023). AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples. Experimental & Molecular Medicine. 55(8). 1734–1742. 1 indexed citations
7.
8.
Jeong, Dabin, et al.. (2022). A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective. International Journal of Molecular Sciences. 23(19). 11498–11498. 5 indexed citations
9.
Hoang, Nam V., Yi Zheng, Muhammad Kamran, et al.. (2020). Identification of Conserved Gene-Regulatory Networks that Integrate Environmental Sensing and Growth in the Root Cambium. Current Biology. 30(15). 2887–2900.e7. 31 indexed citations
10.
Yaghi, Shadi, Koto Ishida, Jose Torres, et al.. (2020). SARS-CoV-2 and Stroke in a New York Healthcare System. Stroke. 51(7). 2002–2011. 442 indexed citations breakdown →
11.
Kim, Jong In, Jeu Park, Yul Ji, et al.. (2019). During Adipocyte Remodeling, Lipid Droplet Configurations Regulate Insulin Sensitivity through F-Actin and G-Actin Reorganization. Molecular and Cellular Biology. 39(20). 35 indexed citations
12.
Han, Eui‐Ryoung, Sun Kim, & Eun‐Kyung Chung. (2019). The Relationship between Medical Students’ Assessment of Standardized Patients and Patient‐Physician Interaction Score. Korean Medical Education Review. 21(3). 150–154. 1 indexed citations
13.
Kim, Sun, et al.. (2017). Gold nanoparticles-based electrochemical method for the detection of protein kinase with a peptide-like inhibitor as the bioreceptor. SHILAP Revista de lepidopterología. 1 indexed citations
14.
Kim, Sun, et al.. (2008). Effect of Weeding and Growth Characteristics by Mulched Plant as Chinese Milk-vetch and Hairy-vetch and Seeding Density of Soybean at Soybean Cultivation in Paddy Field. Korean Journal of Weed Science. 28(2). 139–145.
15.
Kim, Sun, et al.. (2008). Weed Emergence in Upland Field of Summer Crop Cultivation in Honam Area. Korean Journal of Weed Science. 28(1). 32–41. 3 indexed citations
16.
Lee, In-Yong, et al.. (2005). Characteristics of Weed Flora and Weed Community on Orchard Field in the Korea. Weed & Turfgrass Science. 25(4). 267–274. 5 indexed citations
17.
Kim, Sun, et al.. (2004). Physio-Ecological Characteristics and Control of Weedy Rice in the Rice Paddy. Korean Journal of Weed Science. 24(1). 56–63. 2 indexed citations
18.
Kim, Sun, et al.. (2002). Weed Emergence of Rice Machine Transplanting Rice Paddy Field in Honam Area. Korean Journal of Weed Science. 22(2). 128–136. 2 indexed citations
19.
Park, Moon Soo, et al.. (1994). Plant regeneration and In vitro tuber enlargement from callus in Pinellia ternata(Thunb.) Breit.. Korean Journal of Medicinal Crop Science. 2(3). 246–250. 1 indexed citations
20.
Kim, Sun, et al.. (1987). NONREDUNDANT ALLOCATION OF FILES IN DISTRIBUTED SYSTEMS. 287–291. 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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