Youngmi Yoon

501 total citations
52 papers, 387 citations indexed

About

Youngmi Yoon is a scholar working on Molecular Biology, Computational Theory and Mathematics and Plant Science. According to data from OpenAlex, Youngmi Yoon has authored 52 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 14 papers in Computational Theory and Mathematics and 6 papers in Plant Science. Recurrent topics in Youngmi Yoon's work include Bioinformatics and Genomic Networks (23 papers), Gene expression and cancer classification (17 papers) and Machine Learning in Bioinformatics (13 papers). Youngmi Yoon is often cited by papers focused on Bioinformatics and Genomic Networks (23 papers), Gene expression and cancer classification (17 papers) and Machine Learning in Bioinformatics (13 papers). Youngmi Yoon collaborates with scholars based in South Korea and United States. Youngmi Yoon's co-authors include Sanghyun Park, Jaegyoon Ahn, Min Oh, Yunku Yeu, Chihyun Park, Hyunjin Kim, Mi‐Hyun Kim, Jong‐Chan Lee, Seok Jong Yu and Sang-Hyun Park and has published in prestigious journals such as Bioinformatics, PLoS ONE and Expert Systems with Applications.

In The Last Decade

Youngmi Yoon

41 papers receiving 353 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Youngmi Yoon South Korea 11 271 147 39 36 27 52 387
Sangseon Lee South Korea 12 235 0.9× 93 0.6× 60 1.5× 54 1.5× 14 0.5× 40 363
Uma D. Vempati United States 13 466 1.7× 180 1.2× 55 1.4× 27 0.8× 17 0.6× 21 587
Liang‐Chin Huang United States 11 202 0.7× 161 1.1× 58 1.5× 22 0.6× 52 1.9× 20 381
Zhaorong Li China 12 430 1.6× 171 1.2× 42 1.1× 87 2.4× 25 0.9× 17 603
Svetlana Bureeva Russia 11 288 1.1× 213 1.4× 21 0.5× 14 0.4× 55 2.0× 18 454
Yongcui Wang China 12 549 2.0× 336 2.3× 26 0.7× 46 1.3× 32 1.2× 38 651
Takeshi Hase Japan 10 289 1.1× 94 0.6× 25 0.6× 16 0.4× 9 0.3× 23 452
Ziqi Pan China 13 263 1.0× 111 0.8× 47 1.2× 48 1.3× 8 0.3× 28 502
Peiran Jiang China 9 369 1.4× 124 0.8× 76 1.9× 50 1.4× 10 0.4× 12 583
Ádám Arany Belgium 11 193 0.7× 159 1.1× 49 1.3× 13 0.4× 15 0.6× 24 378

Countries citing papers authored by Youngmi Yoon

Since Specialization
Citations

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

Fields of papers citing papers by Youngmi Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Youngmi Yoon

This figure shows the co-authorship network connecting the top 25 collaborators of Youngmi Yoon. A scholar is included among the top collaborators of Youngmi 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 Youngmi Yoon. Youngmi 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.
Kim, Tae‐Heon, et al.. (2022). De Novo Transcriptome Assembly and SNP Discovery for the Development of dCAPS Markers in Oat. Agronomy. 12(1). 184–184. 4 indexed citations
2.
Yoon, Youngmi, et al.. (2020). Drug Repositioning through Drug-Disease Bipartite Network. The Journal of Korean Institute of Information Technology. 18(12). 1–9. 1 indexed citations
3.
Yoon, Youngmi, et al.. (2018). A Verification about the Formation Process of Filter Bubble with Personalization Algorithm. Journal of Korea Multimedia Society. 21(3). 369–381. 3 indexed citations
4.
Yoon, Youngmi, et al.. (2018). Co-occurrence Based Drug-disease Relationship Inference with Genes as Mediators. The Journal of Korean Institute of Information Technology. 16(11). 1–9. 2 indexed citations
5.
Park, Chihyun, So Jeong Yun, Sung Jin Ryu, et al.. (2017). Systematic identification of an integrative network module during senescence from time-series gene expression. BMC Systems Biology. 11(1). 36–36. 8 indexed citations
6.
Oh, Min, et al.. (2017). Drug voyager: a computational platform for exploring unintended drug action. BMC Bioinformatics. 18(1). 131–131. 9 indexed citations
7.
Yeu, Yunku, Youngmi Yoon, & Sanghyun Park. (2015). Protein localization vector propagation: a method for improving the accuracy of drug repositioning. Molecular BioSystems. 11(7). 2096–2102. 47 indexed citations
8.
Kim, Hyunjin, et al.. (2015). LGscore: A method to identify disease-related genes using biological literature and Google data. Journal of Biomedical Informatics. 54. 270–282. 15 indexed citations
9.
Kim, Hyunjin, et al.. (2015). A method of extracting disease-related microRNAs through the propagation algorithm using the environmental factor based global miRNA network. Bio-Medical Materials and Engineering. 26(1_suppl). S1763–72. 23 indexed citations
10.
Kim, Hyunjin, Youngmi Yoon, Jaegyoon Ahn, & Sanghyun Park. (2015). A literature-driven method to calculate similarities among diseases. Computer Methods and Programs in Biomedicine. 122(2). 108–122. 11 indexed citations
11.
Kim, Hyunjin, Jaegyoon Ahn, Chihyun Park, Youngmi Yoon, & Sanghyun Park. (2013). ICP: A novel approach to predict prognosis of prostate cancer with inner-class clustering of gene expression data. Computers in Biology and Medicine. 43(10). 1363–1373. 6 indexed citations
12.
Ahn, Jaegyoon, Dae Hyun Lee, Youngmi Yoon, Yunku Yeu, & Sanghyun Park. (2013). Improved method for protein complex detection using bottleneck proteins. BMC Medical Informatics and Decision Making. 13(S1). S5–S5. 4 indexed citations
13.
Yoon, Youngmi, et al.. (2011). Pattern Analysis and Prediction System for Meme data. The Journal of Korean Institute of Information Technology. 9(9). 163–177.
14.
Yoon, Youngmi, et al.. (2011). The Effect of Airport Service Recovery on Satisfaction, Brand Image and Complaints Intention. 9(3). 121–136. 1 indexed citations
15.
Yoon, Youngmi, et al.. (2011). TC-VGC: A Tumor Classification System using Variations in Genes’ Correlation. Computer Methods and Programs in Biomedicine. 104(3). e87–e101. 5 indexed citations
16.
Park, Chihyun, Jaegyoon Ahn, Youngmi Yoon, & Sanghyun Park. (2011). A Multi-Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High-Resolution aCGH Data. PLoS ONE. 6(10). e26975–e26975. 5 indexed citations
17.
Lee, Young Ho, et al.. (2009). Development of Mining model through reproducibility assessment in Adverse drug event surveillance system. Journal of the Korea Society of Computer and Information. 14(3). 183–192. 2 indexed citations
18.
Yoon, Youngmi, et al.. (2007). Building a Classifier for Integrated Microarray Datasets through Two-Stage Approach. 34(1). 46–58. 1 indexed citations
19.
Yoon, Youngmi, et al.. (2006). Personal and Environmental Predictors of Internet Addiction in Higher Grade Elementary School Students. Journal of Korean Academy of Child Health Nursing. 12(1). 34–43. 4 indexed citations
20.
Yoon, Youngmi & U Kang. (2005). Recovery techniques for Main-Memory database system. 한국정보기술학회논문지. 3(5). 73–88. 2 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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