Byung-Jun Yoon

2.7k total citations
131 papers, 1.7k citations indexed

About

Byung-Jun Yoon is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Byung-Jun Yoon has authored 131 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Molecular Biology, 26 papers in Computational Theory and Mathematics and 24 papers in Artificial Intelligence. Recurrent topics in Byung-Jun Yoon's work include Bioinformatics and Genomic Networks (36 papers), Genomics and Phylogenetic Studies (26 papers) and RNA and protein synthesis mechanisms (24 papers). Byung-Jun Yoon is often cited by papers focused on Bioinformatics and Genomic Networks (36 papers), Genomics and Phylogenetic Studies (26 papers) and RNA and protein synthesis mechanisms (24 papers). Byung-Jun Yoon collaborates with scholars based in United States, South Korea and Qatar. Byung-Jun Yoon's co-authors include P. P. Vaidyanathan, Edward R. Dougherty, Sayed Mohammad Ebrahim Sahraeian, Xiaoning Qian, Junjie Su, Roozbeh Dehghannasiri, Francis J. Alexander, Mohammad Shahrokh Esfahani, Man S. Kim and Huifeng Zhu and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Byung-Jun Yoon

122 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Byung-Jun Yoon United States 20 1.2k 267 211 123 95 131 1.7k
Gunnar W. Klau Germany 24 1.5k 1.3× 339 1.3× 263 1.2× 123 1.0× 139 1.5× 79 2.5k
Qiao Liu China 23 1.1k 0.9× 219 0.8× 212 1.0× 195 1.6× 82 0.9× 117 1.9k
Laurent Jacob France 16 905 0.8× 369 1.4× 314 1.5× 113 0.9× 166 1.7× 30 1.7k
Rui Kuang United States 24 987 0.9× 148 0.6× 249 1.2× 115 0.9× 90 0.9× 74 1.6k
Seonwoo Min South Korea 17 1.7k 1.5× 128 0.5× 262 1.2× 126 1.0× 89 0.9× 32 2.3k
Alfonso Rodríguez‐Patón Spain 25 1.7k 1.5× 557 2.1× 262 1.2× 83 0.7× 96 1.0× 86 2.2k
Xiaolei Zhu China 23 1.1k 1.0× 283 1.1× 188 0.9× 141 1.1× 36 0.4× 64 1.6k
Bo Liao China 29 1.6k 1.3× 216 0.8× 253 1.2× 155 1.3× 79 0.8× 111 2.1k
Tanel Pärnamaa Estonia 5 599 0.5× 132 0.5× 228 1.1× 61 0.5× 78 0.8× 8 1.2k
Xinghua Shi United States 22 745 0.6× 129 0.5× 290 1.4× 225 1.8× 64 0.7× 78 1.7k

Countries citing papers authored by Byung-Jun Yoon

Since Specialization
Citations

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

Fields of papers citing papers by Byung-Jun Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Byung-Jun Yoon. 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 Byung-Jun Yoon. The network helps show where Byung-Jun Yoon may publish in the future.

Co-authorship network of co-authors of Byung-Jun Yoon

This figure shows the co-authorship network connecting the top 25 collaborators of Byung-Jun Yoon. A scholar is included among the top collaborators of Byung-Jun Yoon 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 Byung-Jun Yoon. Byung-Jun Yoon 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.
Urban, Nathan M., et al.. (2024). Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–6.
2.
Yoon, Byung-Jun, et al.. (2024). Location-based electrotactile feedback localizes hitting point in virtual-reality table tennis game. Biomedical Engineering Letters. 14(3). 593–604. 2 indexed citations
3.
Pouchard, Line, Kristofer G. Reyes, Francis J. Alexander, & Byung-Jun Yoon. (2023). A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows. Digital Discovery. 2(5). 1251–1258. 5 indexed citations
4.
5.
Chen, Xuejin, et al.. (2023). Neural message-passing for objective-based uncertainty quantification and optimal experimental design. Engineering Applications of Artificial Intelligence. 123. 106171–106171. 1 indexed citations
6.
Qian, Xiaoning, Li Lynn Tan, Shantenu Jha, et al.. (2023). Optimal decision-making in high-throughput virtual screening pipelines. Patterns. 4(11). 100875–100875. 5 indexed citations
7.
Allam, Omar, et al.. (2022). Uncovering Molecular Structure – Redox Potential Relationships for Organic Electrode Materials: A Hybrid DFT – Machine Learning Approach. ECS Meeting Abstracts. MA2022-02(2). 165–165. 1 indexed citations
8.
Zhao, Guang, Edward R. Dougherty, Byung-Jun Yoon, Francis J. Alexander, & Xiaoning Qian. (2021). Efficient Active Learning for Gaussian Process Classification by Error Reduction. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 8 indexed citations
9.
Yoon, Byung-Jun, et al.. (2020). NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families. PLoS ONE. 15(1). e0227598–e0227598. 4 indexed citations
10.
11.
Kim, Man S., Huan Zhang, Huijuan Yan, Byung-Jun Yoon, & Won‐Bo Shim. (2018). Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses. Scientific Reports. 8(1). 8310–8310. 13 indexed citations
12.
Yoon, Byung-Jun, Xiaoning Qian, & Tamer Kahveci. (2017). Selected research articles from the 2016 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC). BMC Bioinformatics. 18(S4). 159–159.
13.
Sahraeian, Sayed Mohammad Ebrahim & Byung-Jun Yoon. (2013). SMETANA: Accurate and Scalable Algorithm for Probabilistic Alignment of Large-Scale Biological Networks. PLoS ONE. 8(7). e67995–e67995. 74 indexed citations
14.
15.
Qian, Xiaoning, Sayed Mohammad Ebrahim Sahraeian, & Byung-Jun Yoon. (2011). Enhancing the accuracy of HMM-based conserved pathway prediction using global correspondence scores. BMC Bioinformatics. 12(S10). S6–S6. 3 indexed citations
16.
Yoon, Byung-Jun. (2011). Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics. BMC Bioinformatics. 12(S1). S18–S18. 23 indexed citations
17.
Esfahani, Mohammad Shahrokh, Byung-Jun Yoon, & Edward R. Dougherty. (2011). Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty. BMC Bioinformatics. 12(S10). S9–S9. 13 indexed citations
18.
Su, Junjie, Byung-Jun Yoon, & Edward R. Dougherty. (2010). Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network. BMC Bioinformatics. 11(S6). S8–S8. 52 indexed citations
19.
Wang, Ying, Vinayak Brahmakshatriya, Huifeng Zhu, et al.. (2009). Identification of differentially expressed miRNAs in chicken lung and trachea with avian influenza virus infection by a deep sequencing approach. BMC Genomics. 10(1). 512–512. 102 indexed citations
20.
Vaidyanathan, P. P. & Byung-Jun Yoon. (2002). Digital filters for gene prediction applications. 306–310 vol.1. 71 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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