Seungjin Na

1.0k total citations
31 papers, 722 citations indexed

About

Seungjin Na is a scholar working on Molecular Biology, Spectroscopy and Organic Chemistry. According to data from OpenAlex, Seungjin Na has authored 31 papers receiving a total of 722 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 22 papers in Spectroscopy and 1 paper in Organic Chemistry. Recurrent topics in Seungjin Na's work include Advanced Proteomics Techniques and Applications (21 papers), Mass Spectrometry Techniques and Applications (17 papers) and Metabolomics and Mass Spectrometry Studies (10 papers). Seungjin Na is often cited by papers focused on Advanced Proteomics Techniques and Applications (21 papers), Mass Spectrometry Techniques and Applications (17 papers) and Metabolomics and Mass Spectrometry Studies (10 papers). Seungjin Na collaborates with scholars based in South Korea, United States and Australia. Seungjin Na's co-authors include Eunok Paek, Nuno Bandeira, Kong‐Joo Lee, Jaeho Jeong, Jaeho Jeong, Cheolju Lee, Hwa‐Young Kim, Seong Won, Vineet Bafna and Sunghee Woo and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Seungjin Na

30 papers receiving 712 citations

Peers

Seungjin Na
Peter F. Doubleday United States
Runsheng Zheng Germany
Can C. Özbal United States
Leo E. Bonilla United States
Eric Pang United States
Christian Atsriku United States
Zeqiang Ma United States
Tom Blackwell United States
Kellye A. Cupp‐Sutton United States
Martin J. Voorbach United States
Peter F. Doubleday United States
Seungjin Na
Citations per year, relative to Seungjin Na Seungjin Na (= 1×) peers Peter F. Doubleday

Countries citing papers authored by Seungjin Na

Since Specialization
Citations

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

Fields of papers citing papers by Seungjin Na

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seungjin Na

This figure shows the co-authorship network connecting the top 25 collaborators of Seungjin Na. A scholar is included among the top collaborators of Seungjin Na 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 Seungjin Na. Seungjin Na 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, Minsuh, Kyung-Cho Cho, Dae Ho Kim, et al.. (2025). Assessing Long-Term Stored Tissues for Multi-Omics Data Quality and Proteogenomics Suitability. Journal of Proteome Research. 24(9). 4563–4574.
2.
Kim, Seong Ho, Byung‐Gyu Kim, Seungjin Na, et al.. (2024). Hidden route of protein damage through oxygen-confined photooxidation. Nature Communications. 15(1). 10873–10873. 11 indexed citations
3.
Na, Seungjin, Deok‐Ho Roh, James E. Vince, et al.. (2024). Oxidative photocatalysis on membranes triggers non-canonical pyroptosis. Nature Communications. 15(1). 4025–4025. 11 indexed citations
4.
Na, Seungjin, et al.. (2023). CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data. Analytical Chemistry. 95(46). 16918–16926. 3 indexed citations
5.
Li, Honglan, Seungjin Na, Kyu‐Baek Hwang, & Eunok Paek. (2022). TIDD: tool-independent and data-dependent machine learning for peptide identification. BMC Bioinformatics. 23(1). 109–109. 3 indexed citations
6.
Na, Seungjin, et al.. (2020). Cataract-Associated New Mutants S175G/H181Q of βΒ2-Crystallin and P24S/S31G of γD-Crystallin Are Involved in Protein Aggregation by Structural Changes. International Journal of Molecular Sciences. 21(18). 6504–6504. 5 indexed citations
7.
Na, Seungjin & Eunok Paek. (2020). Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions. Computational and Structural Biotechnology Journal. 18. 1391–1402. 19 indexed citations
8.
Na, Seungjin, Jae‐Jin Lee, Jong Wha J. Joo, Kong‐Joo Lee, & Eunok Paek. (2019). deMix: Decoding Deuterated Distributions from Heterogeneous Protein States via HDX-MS. Scientific Reports. 9(1). 3176–3176. 14 indexed citations
9.
Won, Seong, Stefano Bonissone, Seungjin Na, Pavel A. Pevzner, & Vineet Bafna. (2017). The Antibody Repertoire of Colorectal Cancer. Molecular & Cellular Proteomics. 16(12). 2111–2124. 5 indexed citations
10.
Na, Seungjin, Samuel Payne, & Nuno Bandeira. (2016). Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks. Molecular & Cellular Proteomics. 15(11). 3501–3512. 6 indexed citations
11.
Na, Seungjin, Eunok Paek, Jong‐Soon Choi, et al.. (2015). Characterization of disulfide bonds by planned digestion and tandem mass spectrometry. Molecular BioSystems. 11(4). 1156–1164. 18 indexed citations
12.
Woo, Sunghee, Seong Won, Seungjin Na, et al.. (2014). Proteogenomic strategies for identification of aberrant cancer peptides using large‐scale next‐generation sequencing data. PROTEOMICS. 14(23-24). 2719–2730. 54 indexed citations
13.
Kim, Kyutae, Seungjin Na, Jun Seok Kim, et al.. (2013). Reinvestigation of Aminoacyl-TRNA Synthetase Core Complex by Affinity Purification-Mass Spectrometry Reveals TARSL2 as a Potential Member of the Complex. PLoS ONE. 8(12). e81734–e81734. 18 indexed citations
14.
Yeom, Jeonghun, Heebum Lee, Kyutae Kim, et al.. (2011). High-throughput peptide quantification using mTRAQ reagent triplex. BMC Bioinformatics. 12(S1). S46–S46. 14 indexed citations
15.
Na, Seungjin, Nuno Bandeira, & Eunok Paek. (2011). Fast Multi-blind Modification Search through Tandem Mass Spectrometry. Molecular & Cellular Proteomics. 11(4). M111.010199–M111.010199. 118 indexed citations
16.
Jeong, Jaeho, Yongsik Jung, Seungjin Na, et al.. (2010). Novel Oxidative Modifications in Redox-Active Cysteine Residues. Molecular & Cellular Proteomics. 10(3). M110.000513–M110.000513. 72 indexed citations
17.
Joo, Jong Wha J., Seungjin Na, Je‐Hyun Baek, Cheolju Lee, & Eunok Paek. (2009). Target-Decoy with Mass Binning: A Simple and Effective Validation Method for Shotgun Proteomics Using High Resolution Mass Spectrometry. Journal of Proteome Research. 9(2). 1150–1156. 8 indexed citations
18.
Jeong, Jaeho, et al.. (2009). New Algorithm for the Identification of Intact Disulfide Linkages Based on Fragmentation Characteristics in Tandem Mass Spectra. Journal of Proteome Research. 9(1). 626–635. 74 indexed citations
19.
Na, Seungjin, Jaeho Jeong, Heejin Park, Kong‐Joo Lee, & Eunok Paek. (2008). Unrestrictive Identification of Multiple Post-translational Modifications from Tandem Mass Spectrometry Using an Error-tolerant Algorithm Based on an Extended Sequence Tag Approach. Molecular & Cellular Proteomics. 7(12). 2452–2463. 43 indexed citations
20.
Kim, Seunghwan, Seungjin Na, Jaeho Jeong, et al.. (2006). MODi : a powerful and convenient web server for identifying multiple post-translational peptide modifications from tandem mass spectra. Nucleic Acids Research. 34(Web Server). W258–W263. 46 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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