Sangin Lee

423 total citations
36 papers, 276 citations indexed

About

Sangin Lee is a scholar working on Statistics and Probability, Control and Systems Engineering and Molecular Biology. According to data from OpenAlex, Sangin Lee has authored 36 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Statistics and Probability, 8 papers in Control and Systems Engineering and 7 papers in Molecular Biology. Recurrent topics in Sangin Lee's work include Statistical Methods and Inference (13 papers), Advanced Statistical Methods and Models (6 papers) and Gene expression and cancer classification (5 papers). Sangin Lee is often cited by papers focused on Statistical Methods and Inference (13 papers), Advanced Statistical Methods and Models (6 papers) and Gene expression and cancer classification (5 papers). Sangin Lee collaborates with scholars based in South Korea, United States and Sweden. Sangin Lee's co-authors include Sunghoon Kwon, Poria Fajri, Mehdi Ferdowsi, Venkata Anand Kishore Prabhala, Yongdai Kim, Patrick Breheny, Jian Huang, Shuangge Ma, Cun-Hui Zhang and Okyoung Na and has published in prestigious journals such as Bioinformatics, Journal of Applied Ecology and IEEE Transactions on Vehicular Technology.

In The Last Decade

Sangin Lee

31 papers receiving 271 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sangin Lee South Korea 9 67 57 52 44 31 36 276
Kezhen Liu China 9 9 0.1× 108 1.9× 8 0.2× 11 0.3× 66 2.1× 39 328
Yuki Sakai Japan 12 9 0.1× 23 0.4× 24 0.5× 57 1.8× 85 477
David Hoffman France 10 34 0.5× 3 0.1× 86 1.7× 17 0.4× 42 1.4× 22 352
Jean Meloche Canada 9 9 0.1× 32 0.6× 82 1.6× 31 0.7× 3 0.1× 23 228
Mathew W. McLean United States 6 6 0.1× 10 0.2× 80 1.5× 39 0.9× 22 0.7× 7 215
Manuel Bogoya Colombia 10 9 0.1× 7 0.1× 27 0.5× 33 0.8× 2 0.1× 31 340
Tiantian Yang China 8 2 0.0× 38 0.7× 81 1.6× 36 0.8× 14 0.5× 19 281
Tarek Elguebaly Canada 9 4 0.1× 23 0.4× 31 0.6× 179 4.1× 12 0.4× 16 285
Inseop Lee South Korea 10 33 0.5× 62 1.1× 54 1.2× 13 0.4× 34 287

Countries citing papers authored by Sangin Lee

Since Specialization
Citations

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

Fields of papers citing papers by Sangin Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sangin Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Sangin Lee. A scholar is included among the top collaborators of Sangin Lee 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 Sangin Lee. Sangin Lee 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.
McGarvey, Jeffery A., Sangin Lee, Olivia M. Smith, et al.. (2025). Assessing foodborne pathogen survival in bird faeces to co‐manage farms for bird conservation, production, and food safety. Journal of Applied Ecology. 62(3). 516–526. 1 indexed citations
2.
Lee, Sangin, et al.. (2025). A Two‐Panel Delphi Study on Risk Factors of Adolescent Suicide in South Korea. Psychology in the Schools. 62(8). 2526–2541.
3.
Lee, Sangin, et al.. (2024). Complete genome sequence of Citrobacter braakii ASE1 generated by PacBio sequencing. Microbiology Resource Announcements. 13(3). e0100023–e0100023. 1 indexed citations
4.
Kwon, Sunghoon, et al.. (2024). Concave penalized linear discriminant analysis on high dimensions. Communications for Statistical Applications and Methods. 31(4). 393–408. 2 indexed citations
5.
Lee, Sangin, et al.. (2023). Chemical transformation of cannabidiol into psychotropic cannabinoids under acidic reaction conditions: Identification of transformed products by GC-MS. Journal of Food and Drug Analysis. 31(1). 165–176. 9 indexed citations
6.
Yu, Jaehyung, et al.. (2021). Content Controlled Spectral Indices for Detection of Hydrothermal Alteration Minerals Based on Machine Learning and Lasso-Logistic Regression Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14. 7435–7447. 5 indexed citations
7.
Lee, Sangin, Youngjo Lee, & Yudi Pawitan. (2018). Sparse pathway-based prediction models for high-throughput molecular data. Computational Statistics & Data Analysis. 126. 125–135. 1 indexed citations
8.
Lee, Sangin, Faming Liang, Ling Cai, & Guanghua Xiao. (2017). Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies. Cancer Informatics. 16. 2817924085–2817924085. 1 indexed citations
9.
Lee, Sangin, Faming Liang, Ling Cai, & Guanghua Xiao. (2017). A two-stage approach of gene network analysis for high-dimensional heterogeneous data. Biostatistics. 19(2). 216–232. 4 indexed citations
10.
Kwon, Sunghoon, Jeongyoun Ahn, Woncheol Jang, Sangin Lee, & Yongdai Kim. (2016). A doubly sparse approach for group variable selection. Annals of the Institute of Statistical Mathematics. 69(5). 997–1025. 4 indexed citations
11.
Kwon, Sunghoon, Sangin Lee, & Okyoung Na. (2016). Tuning parameter selection for the adaptive LASSO in the autoregressive model. Journal of the Korean Statistical Society. 46(2). 285–297. 16 indexed citations
12.
Huang, Jian, Patrick Breheny, Sangin Lee, Shuangge Ma, & Cun-Hui Zhang. (2015). The Mnet method for variable selection. Statistica Sinica. 31 indexed citations
13.
Lee, Sangin, Yudi Pawitan, Erik Ingelsson, & Youngjo Lee. (2015). Sparse estimation of gene–gene interactions in prediction models. Statistical Methods in Medical Research. 26(5). 2319–2332. 2 indexed citations
14.
Lee, Sangin, Yudi Pawitan, & Youngjo Lee. (2015). A random-effect model approach for group variable selection. Computational Statistics & Data Analysis. 89. 147–157. 1 indexed citations
15.
16.
Yi, SoJeong, Howard Lee, Sangin Lee, et al.. (2014). Korean, Japanese, and Chinese populations featured similar genes encoding drug-metabolizing enzymes and transporters. Pharmacogenetics and Genomics. 24(10). 477–485. 16 indexed citations
17.
Huang, Jian, Patrick Breheny, Sangin Lee, Shuangge Ma, & Cun‐Hui Zhang. (2013). Balancing stability and bias reduction in variable selection with the Mnet estimator. 1 indexed citations
18.
Lee, Sangin, Yongdai Kim, & Sunghoon Kwon. (2012). Quadratic approximation for nonconvex penalized estimations with a diverging number of parameters. Statistics & Probability Letters. 82(9). 1710–1717. 3 indexed citations
19.
Kim, Jin‐Seog, et al.. (2009). Boosting on the functional ANOVA decomposition. Statistics and Its Interface. 2(3). 361–368. 3 indexed citations
20.
Cho, Han-Ku, et al.. (2000). Patterning 220-nm pitch DRAM patterns by using double mask exposure. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4000. 283–283. 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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