Min‐Seok Kwon

3.3k total citations · 1 hit paper
24 papers, 867 citations indexed

About

Min‐Seok Kwon is a scholar working on Molecular Biology, Genetics and Spectroscopy. According to data from OpenAlex, Min‐Seok Kwon has authored 24 papers receiving a total of 867 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 11 papers in Genetics and 4 papers in Spectroscopy. Recurrent topics in Min‐Seok Kwon's work include Bioinformatics and Genomic Networks (11 papers), Genetic Associations and Epidemiology (9 papers) and Gene expression and cancer classification (8 papers). Min‐Seok Kwon is often cited by papers focused on Bioinformatics and Genomic Networks (11 papers), Genetic Associations and Epidemiology (9 papers) and Gene expression and cancer classification (8 papers). Min‐Seok Kwon collaborates with scholars based in South Korea, United States and Sri Lanka. Min‐Seok Kwon's co-authors include Taesung Park, Peter J. Park, Craig L. Bohrson, Christopher A. Walsh, Eminy H.Y. Lee, Rachel E. Rodin, Maxwell A. Sherman, Alison R. Barton, Lovelace J. Luquette and Michael E. Coulter and has published in prestigious journals such as Science, Nature Genetics and Bioinformatics.

In The Last Decade

Min‐Seok Kwon

23 papers receiving 854 citations

Hit Papers

Aging and neurodegeneration are associated with increased... 2017 2026 2020 2023 2017 100 200 300
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. Aging and neurodegeneration are associated with increased mutations in single human neurons
    2017 389 citations Michael A. Lodato, Rachel E. Rodin et al. Science profile →

Peers

Min‐Seok Kwon
S. Stephen Yi United States
Yanxiao Zhang United States
Anders M. Hinsby Denmark
Ellen Gelfand United States
Nurhan Özlü Türkiye
Nur Yucer United States
Giselle R. Wiggin United Kingdom
Igor A. Gak Germany
Pamela Völkel France
Joseph Tcherkezian Canada
S. Stephen Yi United States
Min‐Seok Kwon
Citations per year, relative to Min‐Seok Kwon Min‐Seok Kwon (= 1×) peers S. Stephen Yi

Countries citing papers authored by Min‐Seok Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Min‐Seok Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min‐Seok Kwon. 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 Min‐Seok Kwon. The network helps show where Min‐Seok Kwon may publish in the future.

Co-authorship network of co-authors of Min‐Seok Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Min‐Seok Kwon. A scholar is included among the top collaborators of Min‐Seok Kwon 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 Min‐Seok Kwon. Min‐Seok Kwon 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.
Sung, Ki Sa, Sang Jun Han, Ji-Hye Lee, et al.. (2024). Identification of Plasma Protein Biomarkers for Predicting Lung Cancer. Anticancer Research. 44(11). 5147–5155. 2 indexed citations
2.
Bizzotto, Sara, Yanmei Dou, Javier Ganz, et al.. (2021). Landmarks of human embryonic development inscribed in somatic mutations. Science. 371(6535). 1249–1253. 55 indexed citations
3.
Kwon, Min‐Seok, Soohyun Lee, Michele Berselli, Chong Chu, & Peter J. Park. (2020). BamSnap: a lightweight viewer for sequencing reads in BAM files. Bioinformatics. 37(2). 263–264. 5 indexed citations
4.
Bohrson, Craig L., Alison R. Barton, Michael A. Lodato, et al.. (2019). Linked-read analysis identifies mutations in single-cell DNA-sequencing data. Nature Genetics. 51(4). 749–754. 63 indexed citations
5.
Lodato, Michael A., Rachel E. Rodin, Craig L. Bohrson, et al.. (2017). Aging and neurodegeneration are associated with increased mutations in single human neurons. Science. 359(6375). 555–559. 389 indexed citations breakdown →
6.
Kwon, Min‐Seok, et al.. (2015). An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits. PLoS ONE. 10(9). e0138700–e0138700. 4 indexed citations
7.
Kwon, Min‐Seok, et al.. (2015). Temporal pattern of acoustic imaging noise asymmetrically modulates activation in the auditory cortex. Hearing Research. 331. 57–68. 3 indexed citations
8.
Kim, Yongkang, Seungyeoun Lee, Min‐Seok Kwon, et al.. (2015). Developing cancer prediction model based on stepwise selection by AUC measure for proteomics data. 1345–1350.
9.
Lee, Seungyeoun, Yongkang Kim, Min‐Seok Kwon, & Taesung Park. (2015). A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype. BioMed Research International. 2015. 1–7. 9 indexed citations
10.
Kwon, Min‐Seok, Mira Park, & Taesung Park. (2014). IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis. BMC Medical Genomics. 7(S1). S6–S6. 21 indexed citations
11.
Kwon, Min‐Seok, Yongkang Kim, Seungyeoun Lee, et al.. (2014). Biomarker development for pancreatic ductal adenocarcinoma using integrated analysis of mRNA and miRNA expression. 55. 273–278. 3 indexed citations
12.
Kwon, Min‐Seok, et al.. (2013). A Modified Entropy-Based Approach for Identifying Gene-Gene Interactions in Case-Control Study. PLoS ONE. 8(7). e69321–e69321. 24 indexed citations
13.
Oh, Sohee, Jaehoon Lee, Min‐Seok Kwon, et al.. (2012). A novel method to identify high order gene-gene interactions in genome-wide association studies: Gene-based MDR. BMC Bioinformatics. 13(S9). S5–S5. 43 indexed citations
14.
Kim, Kyunga, et al.. (2012). GxG-Viztool: A program for visualizing gene-gene interactions in genetic association analysis. 54. 838–843. 1 indexed citations
15.
16.
Kwon, Min‐Seok, et al.. (2009). Correction: Molecular Time-Course and the Metabolic Basis of Entry into Dauer in Caenorhabditis elegans. PLoS ONE. 4(2). 2 indexed citations
17.
Lee, Hyoungjoo, Keun Na, Min‐Seok Kwon, et al.. (2009). Quantitative analysis of phosphopeptides in search of the disease biomarker from the hepatocellular carcinoma specimen. PROTEOMICS. 9(12). 3395–3408. 48 indexed citations
18.
Lee, Hyoungjoo, Min‐Seok Kwon, Eunyoung Lee, Sang Yun Cho, & Young‐Ki Paik. (2008). Establishment of a PF2D‐MS/MS platform for rapid profiling and semiquantitative analysis of membrane protein biomarkers. PROTEOMICS. 8(11). 2168–2177. 18 indexed citations
19.
Lee, Eminy H.Y., et al.. (2006). Stabilization of protein encapsulated in poly(lactide-co-glycolide) microspheres by novel viscous S/W/O/W method. International Journal of Pharmaceutics. 331(1). 27–37. 43 indexed citations
20.
Lee, Hyunjoo, Eminy H.Y. Lee, Min‐Seok Kwon, & Young‐Ki Paik. (2006). Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics. Current Opinion in Chemical Biology. 10(1). 42–49. 83 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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