Junhee Seok

6.2k total citations · 1 hit paper
87 papers, 1.2k citations indexed

About

Junhee Seok is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Junhee Seok has authored 87 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 26 papers in Artificial Intelligence and 16 papers in Computer Vision and Pattern Recognition. Recurrent topics in Junhee Seok's work include Gene expression and cancer classification (15 papers), Generative Adversarial Networks and Image Synthesis (8 papers) and Bioinformatics and Genomic Networks (8 papers). Junhee Seok is often cited by papers focused on Gene expression and cancer classification (15 papers), Generative Adversarial Networks and Image Synthesis (8 papers) and Bioinformatics and Genomic Networks (8 papers). Junhee Seok collaborates with scholars based in South Korea, United States and Japan. Junhee Seok's co-authors include Yaeri Kim, Taewoo Roh, Minhyeok Lee, Wenzhong Xiao, Ronald W. Davis, Michael Mindrinos, Stacey Champion, Shanaz H. Dairkee, Aejaz Sayeed and William H. Goodson and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Junhee Seok

79 papers receiving 1.2k citations

Hit Papers

Unveiling ways to reach organic purchase: Green perceived... 2022 2026 2023 2024 2022 50 100 150 200 250
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. Unveiling ways to reach organic purchase: Green perceived value, perceived knowledge, attitude, subjective norm, and trust
    2022 299 citations Junhee Seok 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
Junhee Seok South Korea 16 264 200 192 119 101 87 1.2k
Quan Yuan China 27 122 0.5× 91 0.5× 135 0.7× 65 0.5× 104 1.0× 90 2.1k
Lifeng Chen China 17 175 0.7× 97 0.5× 119 0.6× 297 2.5× 24 0.2× 52 1.7k
Weiqing Wang China 24 63 0.2× 137 0.7× 798 4.2× 245 2.1× 62 0.6× 80 2.0k
Han Liu China 17 56 0.2× 220 1.1× 314 1.6× 70 0.6× 304 3.0× 57 1.8k
Fengmei Yang China 15 88 0.3× 119 0.6× 66 0.3× 7 0.1× 53 0.5× 71 1.1k
Xu Huang China 19 42 0.2× 91 0.5× 217 1.1× 36 0.3× 132 1.3× 97 1.6k
Zhengxin Chen United States 15 39 0.1× 239 1.2× 453 2.4× 134 1.1× 34 0.3× 118 1.3k

Countries citing papers authored by Junhee Seok

Since Specialization
Citations

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

Fields of papers citing papers by Junhee Seok

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junhee Seok

This figure shows the co-authorship network connecting the top 25 collaborators of Junhee Seok. A scholar is included among the top collaborators of Junhee Seok 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 Junhee Seok. Junhee Seok 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.
Kim, Jeongeun, et al.. (2025). Adversarial environment design for crowd navigation based on deep reinforcement learning. Engineering Applications of Artificial Intelligence. 159. 111621–111621.
2.
Seok, Junhee, Yanghee Kim, & Yun Kyung Oh. (2024). How ESG shapes firm value: The mediating role of customer satisfaction. Technological Forecasting and Social Change. 208. 123714–123714. 29 indexed citations
3.
Lee, Minhyeok, et al.. (2024). Stabilized GAN models training with kernel-histogram transformation and probability mass function distance. Applied Soft Computing. 164. 112003–112003. 6 indexed citations
4.
5.
6.
Seok, Junhee, et al.. (2024). Crisis to Opportunity: The Role of Consumer Awareness in Mergers and Acquisitions (M&As) during the COVID-19 Pandemic. SHILAP Revista de lepidopterología. 26(1). 11–22. 1 indexed citations
7.
Lee, Minhyeok, et al.. (2023). ICEGAN: inverse covariance estimating generative adversarial network. Machine Learning Science and Technology. 4(2). 25008–25008. 13 indexed citations
8.
Lee, Minhyeok, et al.. (2023). Deep learning model with L1 penalty for predicting breast cancer metastasis using gene expression data. Machine Learning Science and Technology. 4(2). 25026–25026. 6 indexed citations
9.
Lee, Minhyeok & Junhee Seok. (2022). Score-Guided Generative Adversarial Networks. Axioms. 11(12). 701–701. 11 indexed citations
10.
Kim, Soojeong, et al.. (2022). Inverse design of nanophotonic devices using generative adversarial networks. Engineering Applications of Artificial Intelligence. 115. 105259–105259. 21 indexed citations
11.
Lee, Minhyeok, et al.. (2022). Deep reinforcement learning with a critic-value-based branch tree for the inverse design of two-dimensional optical devices. Applied Soft Computing. 127. 109386–109386. 13 indexed citations
12.
Kim, In-Soo, So Jeong Park, Mun‐Bo Shim, et al.. (2022). Simulator acceleration and inverse design of fin field-effect transistors using machine learning. Scientific Reports. 12(1). 1140–1140. 15 indexed citations
13.
Lee, Minhyeok & Junhee Seok. (2021). Estimation with Uncertainty via Conditional Generative Adversarial Networks. Sensors. 21(18). 6194–6194. 13 indexed citations
14.
Lee, Minhyeok, et al.. (2020). Improved recurrent generative adversarial networks with regularization techniques and a controllable framework. Information Sciences. 538. 428–443. 12 indexed citations
15.
Choi, Sun-Ho, Minhyeok Lee, Young Soo Park, et al.. (2020). Clinical Implication of Concordant or Discordant Genomic Profiling between Primary and Matched Metastatic Tissues in Patients with Colorectal Cancer. Cancer Research and Treatment. 52(3). 764–778. 6 indexed citations
16.
Seok, Junhee, et al.. (2020). Simulation acceleration for transmittance of electromagnetic waves in 2D slit arrays using deep learning. Scientific Reports. 10(1). 10535–10535. 8 indexed citations
17.
Lee, Jeong Hyeon, et al.. (2020). Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records. Scientific Reports. 10(1). 20265–20265. 34 indexed citations
18.
Lee, Minhyeok, et al.. (2019). Portfolio management via two-stage deep learning with a joint cost. Expert Systems with Applications. 143. 113041–113041. 32 indexed citations
19.
Lee, Minhyeok & Junhee Seok. (2019). Controllable Generative Adversarial Network. IEEE Access. 7. 28158–28169. 54 indexed citations
20.
Dairkee, Shanaz H., Junhee Seok, Stacey Champion, et al.. (2008). Bisphenol A Induces a Profile of Tumor Aggressiveness in High-Risk Cells from Breast Cancer Patients. Cancer Research. 68(7). 2076–2080. 96 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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