Kyuri Jo

462 total citations
23 papers, 292 citations indexed

About

Kyuri Jo is a scholar working on Molecular Biology, Computational Theory and Mathematics and Epidemiology. According to data from OpenAlex, Kyuri Jo has authored 23 papers receiving a total of 292 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 10 papers in Computational Theory and Mathematics and 4 papers in Epidemiology. Recurrent topics in Kyuri Jo's work include Bioinformatics and Genomic Networks (11 papers), Computational Drug Discovery Methods (10 papers) and Gene expression and cancer classification (5 papers). Kyuri Jo is often cited by papers focused on Bioinformatics and Genomic Networks (11 papers), Computational Drug Discovery Methods (10 papers) and Gene expression and cancer classification (5 papers). Kyuri Jo collaborates with scholars based in South Korea, United States and Armenia. Kyuri Jo's co-authors include Sun Kim, Hawk-Bin Kwon, Jae Bum Kim, Inuk Jung, Yong Geun Jeon, Sung Sik Choe, Kyung Cheul Shin, Hyejin Kang, Jeu Park and Hongryul Ahn and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Bioinformatics.

In The Last Decade

Kyuri Jo

21 papers receiving 287 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kyuri Jo South Korea 9 160 64 62 38 38 23 292
Sangsoo Lim South Korea 13 334 2.1× 64 1.0× 151 2.4× 31 0.8× 69 1.8× 26 481
Aarti Singh United Kingdom 8 225 1.4× 43 0.7× 13 0.2× 32 0.8× 30 0.8× 12 341
Chuang Kee Ong United Kingdom 3 202 1.3× 26 0.4× 55 0.9× 13 0.3× 30 0.8× 3 320
Yongpan An China 6 153 1.0× 34 0.5× 62 1.0× 27 0.7× 14 0.4× 8 268
Ab Rauf Shah United States 8 187 1.2× 43 0.7× 21 0.3× 45 1.2× 27 0.7× 14 401
Shuba Gopal United States 8 346 2.2× 56 0.9× 11 0.2× 49 1.3× 40 1.1× 11 425
Janica L. Wiederstein Germany 9 320 2.0× 77 1.2× 9 0.1× 44 1.2× 50 1.3× 9 507
Mayumi Yonemochi Japan 9 257 1.6× 20 0.3× 41 0.7× 31 0.8× 14 0.4× 10 398

Countries citing papers authored by Kyuri Jo

Since Specialization
Citations

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

Fields of papers citing papers by Kyuri Jo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kyuri Jo

This figure shows the co-authorship network connecting the top 25 collaborators of Kyuri Jo. A scholar is included among the top collaborators of Kyuri Jo 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 Kyuri Jo. Kyuri Jo 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.
Park, Juho, et al.. (2025). Comparative analysis of regression algorithms for drug response prediction using GDSC dataset. BMC Research Notes. 18(S1). 10–10.
2.
Rajapakse, Jagath C., et al.. (2025). Positional embeddings and zero-shot learning using BERT for molecular-property prediction. Journal of Cheminformatics. 17(1). 17–17. 1 indexed citations
3.
Jang, H. J., et al.. (2025). ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing. Computers in Biology and Medicine. 187. 109729–109729.
4.
Jo, Kyuri, et al.. (2024). Graph Neural Networks with Multi-features for Predicting Cocrystals using APIs and Coformers Interactions. Current Medicinal Chemistry. 31(36). 5953–5968. 1 indexed citations
5.
Jo, Kyuri, et al.. (2024). Graph Neural Network and BERT Model for Antimalarial Drug Predictions Using Plasmodium Potential Targets. Applied Sciences. 14(4). 1472–1472. 2 indexed citations
6.
Jeon, Yong Geun, Hahn Nahmgoong, Jiyoung Oh, et al.. (2024). Ubiquitin ligase RNF20 coordinates sequential adipose thermogenesis with brown and beige fat-specific substrates. Nature Communications. 15(1). 940–940. 3 indexed citations
7.
Kim, Mi-Hye, et al.. (2023). NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection. IEEE Access. 11. 140740–140751. 4 indexed citations
8.
Han, Ji Seul, Yong Geun Jeon, Gung Lee, et al.. (2022). Adipocyte HIF2α functions as a thermostat via PKA Cα regulation in beige adipocytes. Nature Communications. 13(1). 3268–3268. 14 indexed citations
9.
Kim, Sun, et al.. (2022). DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer. International Journal of Molecular Sciences. 23(22). 13919–13919. 31 indexed citations
10.
Jo, Kyuri, et al.. (2021). Inferring transcriptomic cell states and transitions only from time series transcriptome data. Scientific Reports. 11(1). 12566–12566. 6 indexed citations
11.
Lee, Sangseon, Sangsoo Lim, Dabin Jeong, et al.. (2020). DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration. Frontiers in Genetics. 11. 564792–564792. 13 indexed citations
12.
13.
Kim, Jong In, Jeu Park, Yul Ji, et al.. (2019). During Adipocyte Remodeling, Lipid Droplet Configurations Regulate Insulin Sensitivity through F-Actin and G-Actin Reorganization. Molecular and Cellular Biology. 39(20). 35 indexed citations
14.
15.
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
16.
Hwang, Injae, Kyuri Jo, Kyung Cheul Shin, et al.. (2019). GABA-stimulated adipose-derived stem cells suppress subcutaneous adipose inflammation in obesity. Proceedings of the National Academy of Sciences. 116(24). 11936–11945. 63 indexed citations
17.
Lim, Sangsoo, et al.. (2017). PINTnet: construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI. BMC Systems Biology. 11(S2). 15–15. 12 indexed citations
18.
Lee, Jusang, Kyuri Jo, Sunwon Lee, Jaewoo Kang, & Sun Kim. (2016). Prioritizing biological pathways by recognizing context in time-series gene expression data. BMC Bioinformatics. 17(S17). 477–477. 7 indexed citations
19.
Jo, Kyuri, et al.. (2016). Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways. Bioinformatics. 32(12). i128–i136. 6 indexed citations
20.
Jo, Kyuri, Hawk-Bin Kwon, & Sun Kim. (2014). Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress. Methods. 67(3). 364–372. 24 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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