Jae Yong Ryu

2.8k total citations · 1 hit paper
41 papers, 1.9k citations indexed

About

Jae Yong Ryu is a scholar working on Molecular Biology, Computational Theory and Mathematics and Plant Science. According to data from OpenAlex, Jae Yong Ryu has authored 41 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 9 papers in Plant Science. Recurrent topics in Jae Yong Ryu's work include Computational Drug Discovery Methods (9 papers), Microbial Metabolic Engineering and Bioproduction (9 papers) and Plant Molecular Biology Research (8 papers). Jae Yong Ryu is often cited by papers focused on Computational Drug Discovery Methods (9 papers), Microbial Metabolic Engineering and Bioproduction (9 papers) and Plant Molecular Biology Research (8 papers). Jae Yong Ryu collaborates with scholars based in South Korea, Denmark and United Kingdom. Jae Yong Ryu's co-authors include Sang Yup Lee, Hyun Uk Kim, Chung‐Mo Park, Hyo‐Jun Lee, Kwang‐Seok Oh, Pil Joon Seo, Jae‐Hoon Jung, Woo Dae Jang, Byung Ho Lee and Jeong Hyun Lee and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Nature Biotechnology.

In The Last Decade

Jae Yong Ryu

39 papers receiving 1.9k citations

Hit Papers

align trajectories

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.

Deep learning improves prediction of drug–drug and drug–food interactions

2018 378 citations Jae Yong Ryu, Hyun Uk Kim et al. Proceedings of the National Academy of Sciences profile →

Peers

Jae Yong Ryu

CTM

Jianhong Gan China

Shailendra K. Gupta India

Thomas Wiese United States

David Allen United States

Jinhyuk Lee South Korea

Terry R. Van Vleet United States

Hyun Woo Kim South Korea

Ida Schomburg Germany

Nashi Widodo Indonesia

Jianhong Gan China

Jae Yong Ryu

626 ×0.5

158 ×0.3

CTM

181 ×0.4

160 ×0.8

76 ×0.5

Citations per year, relative to Jae Yong Ryu Jae Yong Ryu (= 1×) peers Jianhong Gan

Countries citing papers authored by Jae Yong Ryu

Since Specialization

Citations

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

Fields of papers citing papers by Jae Yong Ryu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jae Yong Ryu. 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 Jae Yong Ryu. The network helps show where Jae Yong Ryu may publish in the future.

Co-authorship network of co-authors of Jae Yong Ryu

This figure shows the co-authorship network connecting the top 25 collaborators of Jae Yong Ryu. A scholar is included among the top collaborators of Jae Yong Ryu 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 Jae Yong Ryu. Jae Yong Ryu 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

Choi, Jeongwhan, et al.. (2025). Predicting drug-drug interactions: A deep learning approach with GCN-based collaborative filtering. Artificial Intelligence in Medicine. 167. 103185–103185.

Cho, Sung Woo, Hyun Uk Kim, Ji Hoon Kim, et al.. (2024). Intrahepatic IgA complex induces polarization of cancer-associated fibroblasts to matrix phenotypes in the tumor microenvironment of HCC. Hepatology. 80(5). 1074–1086. 7 indexed citations

Choi, Yongwook, et al.. (2024). Analysis of host factor networks during hepatitis B virus infection in primary human hepatocytes. Virology Journal. 21(1). 170–170. 1 indexed citations

Gu, Changdai, Woo Dae Jang, Kwangseok Oh, & Jae Yong Ryu. (2024). AnoChem: Prediction of chemical structural abnormalities based on machine learning models. Computational and Structural Biotechnology Journal. 23. 2116–2121.

Kim, Won Jun, Youngjoon Lee, Hyun Uk Kim, et al.. (2023). Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes. Cell Systems. 14(11). 990–1001.e5. 5 indexed citations

Cho, Jae Sung, Dongsoo Yang, Cindy Pricilia Surya Prabowo, et al.. (2023). Targeted and high-throughput gene knockdown in diverse bacteria using synthetic sRNAs. Nature Communications. 14(1). 2359–2359. 36 indexed citations

Kim, Yeji, Jae Yong Ryu, Hyun Uk Kim, & Sang Yup Lee. (2023). Computational prediction of interactions between Paxlovid and prescription drugs. Proceedings of the National Academy of Sciences. 120(12). e2221857120–e2221857120. 2 indexed citations

Kim, Yeji, Jae Yong Ryu, Hyun Uk Kim, Woo Dae Jang, & Sang Yup Lee. (2021). A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis. Biotechnology Journal. 16(5). e2000605–e2000605. 20 indexed citations

Lim, Gyutae, Chae Jo Lim, Jeong Hyun Lee, et al.. (2021). Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach. Scientific Reports. 11(1). 17138–17138. 5 indexed citations

10.

Jang, Woo Dae, Tae Yong Kim, Hyun Uk Kim, et al.. (2019). Genomic and metabolic analysis of Komagataeibacter xylinus DSM 2325 producing bacterial cellulose nanofiber. Biotechnology and Bioengineering. 116(12). 3372–3381. 65 indexed citations

11.

Ryu, Jae Yong, Hyun Uk Kim, & Sang Yup Lee. (2019). Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proceedings of the National Academy of Sciences. 116(28). 13996–14001. 183 indexed citations

12.

Yang, Dongsoo, Seung Min Yoo, Changdai Gu, et al.. (2019). Expanded synthetic small regulatory RNA expression platforms for rapid and multiplex gene expression knockdown. Metabolic Engineering. 54. 180–190. 42 indexed citations

13.

Tong, Tong, Si Chen, Lianrong Wang, et al.. (2018). Occurrence, evolution, and functions of DNA phosphorothioate epigenetics in bacteria. Proceedings of the National Academy of Sciences. 115(13). E2988–E2996. 69 indexed citations

14.

Ryu, Jae Yong, Hyun Uk Kim, & Sang Yup Lee. (2018). Deep learning improves prediction of drug–drug and drug–food interactions. Proceedings of the National Academy of Sciences. 115(18). E4304–E4311. 378 indexed citations breakdown →

15.

Hur, Wonhee, Jae Yong Ryu, Hyun Uk Kim, et al.. (2017). Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133. Scientific Reports. 7(1). 45557–45557. 35 indexed citations

16.

Jang, Woo Dae, et al.. (2017). Bacterial cellulose as an example product for sustainable production and consumption. Microbial Biotechnology. 10(5). 1181–1185. 53 indexed citations

17.

Ryu, Jae Yong, Hyun Uk Kim, & Sang Yup Lee. (2015). Human genes with a greater number of transcript variants tend to show biological features of housekeeping and essential genes. Molecular BioSystems. 11(10). 2798–2807. 9 indexed citations

18.

Kim, Hyun Uk, et al.. (2015). A systems approach to traditional oriental medicine. Nature Biotechnology. 33(3). 264–268. 93 indexed citations

19.

Yon, Felipe, et al.. (2012). Identification and characterization of circadian clock genes in a native tobacco, Nicotiana attenuata. BMC Plant Biology. 12(1). 172–172. 25 indexed citations

20.

Hong, Shin‐Young, et al.. (2012). A Competitive Peptide Inhibitor KIDARI Negatively Regulates HFR1 by Forming Nonfunctional Heterodimers in Arabidopsis Photomorphogenesis. Molecules and Cells. 35(1). 25–31. 32 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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