Jae Yong Ryu

2.8k citations
41 papers · 1.9k indexed · 1 hit paper · h-index 24

Jae Yong Ryu

39 papers receiving 1.9k citations

Hit Papers

Deep learning improves prediction of drug–drug and drug–f...3782018202620202023100200300

Peers

Jae Yong Ryu
Comparison fields: 5 of 142
  • Computational Theory and Mathematics 516
  • Molecular Biology 1.3k
  • Pharmacology 127
  • Plant Science 464
  • Toxicology 33
Replace Jinhyuk Lee with:
Jinhyuk Lee South Korea
Jianhong Gan China
Hua‐Wu Zeng China
Nashi Widodo Indonesia
Shan Feng China
Thomas Wiese United States
Hyun Woo Kim South Korea
Shahid Ali South Korea
Li Fu China
Ying‐Kun Qiu China
Jae Yong Ryu relative to Jinhyuk Lee South Korea Jinhyuk Lee's profile →
Citations per field
00.5×
Jinhyuk Lee · 1×
Citations per year

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

The 25 scholars most cited alongside Jae Yong Ryu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jae Yong Ryu Line = papers co-authored together Jae Yong Ryu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20247
3 20241
4 20240
5 20235
6 202336
7 20232
8 202120
9 20215
10 201965
11 2019183
12 201942
13 201869
14
Deep learning improves prediction of drug–drug and drug–food interactionsbreakdown →
2018378
15 201735
16 201753
17 20159
18 201593
19 201225
20 201232

About Jae Yong Ryu

Jae Yong Ryu is a scholar working on Computational Theory and Mathematics, Applied Microbiology and Biotechnology and Pharmacology, having authored 41 papers that have together received 1.9k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Microbial Metabolic Engineering and Bioproduction (9 papers), Plant Molecular Biology Research (8 papers), Plant Reproductive Biology (6 papers), Light effects on plants (4 papers), Enzyme Catalysis and Immobilization (4 papers), Bioinformatics and Genomic Networks (4 papers) and Photosynthetic Processes and Mechanisms (4 papers). The work is most often cited by research in Computational Theory and Mathematics (516 citations), Molecular Biology (1.3k citations) and Pharmacology (127 citations). Jae Yong Ryu has collaborated with scholars based in South Korea, Denmark and United Kingdom. Frequent 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. Their work appears in journals such as Proceedings of the National Academy of Sciences, Bioinformatics, Scientific Reports, Molecules and Cells and Molecular Plant.

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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