Hongryul Ahn

988 total citations
22 papers, 440 citations indexed

About

Hongryul Ahn is a scholar working on Molecular Biology, Plant Science and Neurology. According to data from OpenAlex, Hongryul Ahn has authored 22 papers receiving a total of 440 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 8 papers in Plant Science and 2 papers in Neurology. Recurrent topics in Hongryul Ahn's work include Bioinformatics and Genomic Networks (7 papers), Plant Molecular Biology Research (6 papers) and Plant Gene Expression Analysis (4 papers). Hongryul Ahn is often cited by papers focused on Bioinformatics and Genomic Networks (7 papers), Plant Molecular Biology Research (6 papers) and Plant Gene Expression Analysis (4 papers). Hongryul Ahn collaborates with scholars based in South Korea and United States. Hongryul Ahn's co-authors include Sun Kim, Sam A. Golden, Inuk Jung, Vaishnav Krishnan, Alfred J. Robison, Deveroux Ferguson, John H. Morrison, Paul Greengard, William G.M. Janssen and Dani Dumitriu and has published in prestigious journals such as Journal of Neuroscience, Bioinformatics and Current Biology.

In The Last Decade

Hongryul Ahn

19 papers receiving 435 citations

Peers

Hongryul Ahn

CMN

Wen‐Chin Huang United States

Silvia Burbassi Italy

Jessica R. Edelson United States

Ana Lima United Kingdom

Klaus Wanisch United Kingdom

Jacobine Kuijlaars Belgium

Hongyu Zhong China

Hasan Mohammad Singapore

Amy S. Beadles-Bohling United States

Miho Nakajima Japan

Wen‐Chin Huang United States

Hongryul Ahn

178 ×0.9

36 ×0.3

102 ×1.0

CMN

13 ×0.1

27 ×0.3

Citations per year, relative to Hongryul Ahn Hongryul Ahn (= 1×) peers Wen‐Chin Huang

Countries citing papers authored by Hongryul Ahn

Since Specialization

Citations

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

Fields of papers citing papers by Hongryul Ahn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hongryul Ahn

This figure shows the co-authorship network connecting the top 25 collaborators of Hongryul Ahn. A scholar is included among the top collaborators of Hongryul 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 Hongryul Ahn. Hongryul Ahn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ahn, Hongryul, et al.. (2025). Discovering Vitamin-D-Deficiency-Associated Factors in Korean Adults Using KNHANES Data Based on an Integrated Analysis of Machine Learning and Statistical Techniques. Nutrients. 17(4). 618–618.

Ahn, Hongryul, et al.. (2025). A Comparative Study of Pixel-Wise Loss Functions for Deep-Learning-Based Lesion Segmentation in Medical Imaging. Journal of Broadcast Engineering. 30(2). 104–111.

Ahn, Hongryul, et al.. (2023). Ensemble Learning to Identify Depression Indicators for Korean Farmers. IEEE Access. 11. 118787–118800. 1 indexed citations

Ahn, Hongryul, et al.. (2021). IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data. 2021 IEEE International Conference on Big Data (Big Data). 2791–2800.

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

Ahn, Hongryul, et al.. (2020). Impact of mutations in DNA methylation modification genes on genome-wide methylation landscapes and downstream gene activations in pan-cancer. BMC Medical Genomics. 13(S3). 27–27. 21 indexed citations

Lee, Youngsun, Hongryul Ahn, Hyeong‐jun Han, et al.. (2020). Functional in vivo and in vitro effects of 20q11.21 genetic aberrations on hPSC differentiation. Scientific Reports. 10(1). 18582–18582. 17 indexed citations

Kang, Dong Won, et al.. (2019). StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis. BMC Genomics. 20(S11). 949–949. 6 indexed citations

Ahn, Hongryul, et al.. (2019). PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation. Frontiers in Plant Science. 10. 698–698. 4 indexed citations

10.

Ahn, Hongryul, Inuk Jung, Heejoon Chae, et al.. (2019). HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis. BMC Bioinformatics. 20(S16). 588–588. 5 indexed citations

11.

Ahn, Hongryul, SeungHyun Son, & Sun Kim. (2019). DeepFunNet: Deep Learning for Gene Functional Similarity Network Construction. 1–8. 1 indexed citations

12.

Kang, Dong Won, et al.. (2018). Identifying stress-related genes and predicting stress types in Arabidopsis using logical correlation layer and CMCL loss through time-series data. 399–404. 3 indexed citations

13.

Ahn, Hongryul, Inuk Jung, Jin‐Woo Park, et al.. (2017). Transcriptional Network Analysis Reveals Drought Resistance Mechanisms of AP2/ERF Transgenic Rice. Frontiers in Plant Science. 8. 1044–1044. 41 indexed citations

14.

Ahn, Hongryul, Heejoon Chae, Woosuk Jung, & Sun Kim. (2017). Integration of heterogeneous time series gene expression data by clustering on time dimension. 332–335. 2 indexed citations

15.

Ahn, Hongryul, Sean Bong Lee, Jong-Yeon Shin, et al.. (2016). Integrated analysis of omics data using microRNA-target mRNA network and PPI network reveals regulation of Gnai1 function in the spinal cord of Ews/Ewsr1 KO mice. BMC Medical Genomics. 9(S1). 33–33. 6 indexed citations

16.

Ahn, Hongryul, et al.. (2016). Novel drought-responsive regulatory coding and non-coding transcripts from Oryza Sativa L.. Genes & Genomics. 38(10). 949–960. 6 indexed citations

17.

Jung, Inuk, Hongryul Ahn, Ju‐Kon Kim, et al.. (2016). Clustering and evolutionary analysis of small RNAs identify regulatory siRNA clusters induced under drought stress in rice. BMC Systems Biology. 10(S4). 115–115. 9 indexed citations

18.

Jung, Inuk, et al.. (2016). TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes. Bioinformatics. 33(23). 3827–3835. 26 indexed citations

19.

Kim, Keunyoung, Yu Jin Hwang, Junho Choe, et al.. (2013). A multifunctional protein EWS regulates the expression of Drosha and microRNAs. Cell Death and Differentiation. 21(1). 136–145. 34 indexed citations

20.

Christoffel, Daniel J., Sam A. Golden, Dani Dumitriu, et al.. (2011). IκB Kinase Regulates Social Defeat Stress-Induced Synaptic and Behavioral Plasticity. Journal of Neuroscience. 31(1). 314–321. 219 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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