Lee Sael

1.9k total citations
60 papers, 1.3k citations indexed

About

Lee Sael is a scholar working on Molecular Biology, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Lee Sael has authored 60 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 13 papers in Materials Chemistry and 12 papers in Artificial Intelligence. Recurrent topics in Lee Sael's work include Protein Structure and Dynamics (17 papers), Machine Learning in Bioinformatics (10 papers) and Tensor decomposition and applications (10 papers). Lee Sael is often cited by papers focused on Protein Structure and Dynamics (17 papers), Machine Learning in Bioinformatics (10 papers) and Tensor decomposition and applications (10 papers). Lee Sael collaborates with scholars based in South Korea, United States and France. Lee Sael's co-authors include Daisuke Kihara, U Kang, Vishwesh Venkatraman, Yi‐Feng Yang, Jinhong Jung, Kijung Shin, Rayan Chikhi, David La, Raif M. Rustamov and Juan Esquivel‐Rodríguez and has published in prestigious journals such as Bioinformatics, PLoS ONE and International Journal of Molecular Sciences.

In The Last Decade

Lee Sael

56 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lee Sael South Korea 21 698 301 261 216 142 60 1.3k
Jiawei Luo China 30 2.3k 3.3× 495 1.6× 105 0.4× 315 1.5× 14 0.1× 154 3.1k
Ke Yan China 20 801 1.1× 139 0.5× 30 0.1× 398 1.8× 30 0.2× 37 1.7k
Erik Nijkamp United States 10 178 0.3× 37 0.1× 39 0.1× 416 1.9× 33 0.2× 22 1.1k
Utz‐Uwe Haus Germany 13 534 0.8× 194 0.6× 18 0.1× 116 0.5× 14 0.1× 28 932
Reinhard Heckel United States 19 1.0k 1.5× 233 0.8× 34 0.1× 331 1.5× 5 0.0× 56 1.6k
Satoshi Matsuoka Japan 30 79 0.1× 79 0.3× 63 0.2× 312 1.4× 12 0.1× 170 3.7k
Chien‐Hung Huang Taiwan 17 452 0.6× 234 0.8× 12 0.0× 207 1.0× 30 0.2× 76 1.1k
Jun Huan United States 16 165 0.2× 166 0.6× 56 0.2× 277 1.3× 3 0.0× 53 694
Rui Kuang United States 24 987 1.4× 148 0.5× 57 0.2× 249 1.2× 4 0.0× 74 1.6k

Countries citing papers authored by Lee Sael

Since Specialization
Citations

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

Fields of papers citing papers by Lee Sael

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lee Sael

This figure shows the co-authorship network connecting the top 25 collaborators of Lee Sael. A scholar is included among the top collaborators of Lee Sael 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 Lee Sael. Lee Sael 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.
Na, Byunggook, et al.. (2023). AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples. Experimental & Molecular Medicine. 55(8). 1734–1742. 1 indexed citations
2.
Lee, Haeun, Min-Soo Kang, Ki Wook Jung, et al.. (2021). An artificial neural network model for predicting frictional pressure drop in micro-pin fin heat sink. Applied Thermal Engineering. 194. 117012–117012. 42 indexed citations
3.
Lee, Haeun, Min-Soo Kang, Daewoong Jung, et al.. (2021). A neural network model for free-falling condensation heat transfer in the presence of non-condensable gases. International Journal of Thermal Sciences. 171. 107202–107202. 35 indexed citations
4.
Choi, Yoon Jin, Jung Hun Ohn, Nayoung Kim, et al.. (2020). Family-based exome sequencing combined with linkage analyses identifies rare susceptibility variants of MUC4 for gastric cancer. PLoS ONE. 15(7). e0236197–e0236197. 2 indexed citations
5.
Lim, Yongsub, et al.. (2019). PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks. BMC Bioinformatics. 20(S13). 381–381. 7 indexed citations
6.
Lee, Jungwoo, et al.. (2018). CTD: Fast, accurate, and interpretable method for static and dynamic tensor decompositions. PLoS ONE. 13(7). e0200579–e0200579. 4 indexed citations
7.
Sael, Lee, et al.. (2018). Impact of structural prior knowledge in SNV prediction: Towards causal variant finding in rare disease. PLoS ONE. 13(9). e0204101–e0204101. 3 indexed citations
8.
Jin, Woojeong, et al.. (2016). Personalized Ranking in Signed Networks Using Signed Random Walk with Restart. IEEE Conference Proceedings. 2016. 978. 3 indexed citations
9.
Xiong, Yi, Juan Esquivel‐Rodríguez, Lee Sael, & Daisuke Kihara. (2014). 3D-SURFER 2.0: Web Platform for Real-Time Search and Characterization of Protein Surfaces. Methods in molecular biology. 1137. 105–117. 10 indexed citations
10.
Kim, Sungchul, Lee Sael, & Hwanjo Yu. (2013). Efficient protein structure search using indexing methods. BMC Medical Informatics and Decision Making. 13(S1). S8–S8. 2 indexed citations
11.
Mullins, E.A., Courtney M. Starks, Julie A. Francois, et al.. (2012). Formyl‐coenzyme A (CoA):oxalate CoA‐transferase from the acidophile Acetobacter aceti has a distinctive electrostatic surface and inherent acid stability. Protein Science. 21(5). 686–696. 18 indexed citations
12.
Sael, Lee & Daisuke Kihara. (2012). Constructing patch-based ligand-binding pocket database for predicting function of proteins. BMC Bioinformatics. 13(S2). S7–S7. 5 indexed citations
13.
Sael, Lee, Meghana Chitale, & Daisuke Kihara. (2012). Structure- and sequence-based function prediction for non-homologous proteins. Journal of Structural and Functional Genomics. 13(2). 111–123. 25 indexed citations
14.
Sael, Lee & Daisuke Kihara. (2011). Detecting local ligand‐binding site similarity in nonhomologous proteins by surface patch comparison. Proteins Structure Function and Bioinformatics. 80(4). 1177–1195. 51 indexed citations
15.
Kihara, Daisuke, Lee Sael, Rayan Chikhi, & Juan Esquivel‐Rodríguez. (2011). Molecular Surface Representation Using 3D Zernike Descriptors for Protein Shape Comparison and Docking. Current Protein and Peptide Science. 12(6). 520–530. 64 indexed citations
16.
Chikhi, Rayan, Lee Sael, & Daisuke Kihara. (2010). Real-time ligand binding pocket database search using local surface descriptors. Proteins Structure Function and Bioinformatics. 78(9). 2007–2028. 48 indexed citations
17.
Venkatraman, Vishwesh, Lee Sael, & Daisuke Kihara. (2009). Potential for Protein Surface Shape Analysis Using Spherical Harmonics and 3D Zernike Descriptors. Cell Biochemistry and Biophysics. 54(1-3). 23–32. 73 indexed citations
18.
Kihara, Daisuke, Lee Sael, & Rayan Chikhi. (2009). Local surface shape-based protein function prediction using Zernike descriptors. Biophysical Journal. 96(3). 650a–650a. 4 indexed citations
19.
Sael, Lee, Bin Li, David La, et al.. (2008). Fast protein tertiary structure retrieval based on global surface shape similarity. Proteins Structure Function and Bioinformatics. 72(4). 1259–1273. 98 indexed citations
20.
Sael, Lee, David La, Bin Li, Raif M. Rustamov, & Daisuke Kihara. (2008). Rapid comparison of properties on protein surface. Proteins Structure Function and Bioinformatics. 73(1). 1–10. 59 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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