Seonwoo Min

3.7k total citations · 1 hit paper
32 papers, 2.3k citations indexed

About

Seonwoo Min is a scholar working on Molecular Biology, Cognitive Neuroscience and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Seonwoo Min has authored 32 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 5 papers in Cognitive Neuroscience and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Seonwoo Min's work include CRISPR and Genetic Engineering (11 papers), RNA and protein synthesis mechanisms (11 papers) and Machine Learning in Bioinformatics (5 papers). Seonwoo Min is often cited by papers focused on CRISPR and Genetic Engineering (11 papers), RNA and protein synthesis mechanisms (11 papers) and Machine Learning in Bioinformatics (5 papers). Seonwoo Min collaborates with scholars based in South Korea, United States and Ethiopia. Seonwoo Min's co-authors include Sungroh Yoon, Byunghan Lee, Seokjoong Kim, Hui Kwon Kim, Jinman Park, Sungtae Lee, Jae Woo Choi, Younggwang Kim, Goosang Yu and Sung‐Rae Cho and has published in prestigious journals such as Cell, Nature Communications and Nature Biotechnology.

In The Last Decade

Seonwoo Min

27 papers receiving 2.3k citations

Hit Papers

Deep learning in bioinformatics 2016 2026 2019 2022 2016 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep learning in bioinformatics
    2016 1.0k citations Seonwoo Min, Byunghan Lee et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seonwoo Min South Korea 17 1.7k 262 261 164 128 32 2.3k
Nicolò Fusi United Kingdom 11 2.9k 1.7× 214 0.8× 560 2.1× 291 1.8× 97 0.8× 16 3.7k
Michael O. Duff United States 19 2.0k 1.2× 260 1.0× 425 1.6× 222 1.4× 50 0.4× 24 2.8k
Xiao Wang China 27 2.2k 1.3× 102 0.4× 510 2.0× 131 0.8× 35 0.3× 120 2.8k
Shaojie Zhang United States 30 2.0k 1.2× 151 0.6× 243 0.9× 320 2.0× 49 0.4× 172 3.4k
Ivan Merelli Italy 22 1.3k 0.7× 93 0.4× 407 1.6× 94 0.6× 79 0.6× 144 2.0k
Xinghua Shi United States 22 745 0.4× 290 1.1× 224 0.9× 109 0.7× 129 1.0× 78 1.7k
Wenjie Shu China 21 1.2k 0.7× 150 0.6× 141 0.5× 79 0.5× 48 0.4× 52 1.7k
Rosalba Giugno Italy 31 1.5k 0.9× 508 1.9× 111 0.4× 74 0.5× 305 2.4× 108 2.8k
Faraz Faghri United States 15 810 0.5× 284 1.1× 182 0.7× 53 0.3× 28 0.2× 27 1.8k

Countries citing papers authored by Seonwoo Min

Since Specialization
Citations

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

Fields of papers citing papers by Seonwoo Min

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seonwoo Min

This figure shows the co-authorship network connecting the top 25 collaborators of Seonwoo Min. A scholar is included among the top collaborators of Seonwoo Min 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 Seonwoo Min. Seonwoo Min 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, Nahye, Sungjae Kim, Jung Hwa Seo, et al.. (2023). Deep learning models to predict the editing efficiencies and outcomes of diverse base editors. Nature Biotechnology. 42(3). 484–497. 39 indexed citations
2.
Kim, Young-hoon, Nahye Kim, Seonwoo Min, et al.. (2023). Sniper2L is a high-fidelity Cas9 variant with high activity. Nature Chemical Biology. 19(8). 972–980. 33 indexed citations
3.
Min, Seonwoo, et al.. (2023). Improving generalization performance of electrocardiogram classification models. Physiological Measurement. 44(5). 54003–54003. 6 indexed citations
4.
Min, Seonwoo, Sungtae Lee, Jung Hwa Seo, et al.. (2023). Massively parallel evaluation and computational prediction of the activities and specificities of 17 small Cas9s. Nature Methods. 20(7). 999–1009. 12 indexed citations
5.
Lim, Jung Min, Seonwoo Min, Dong Young Kim, et al.. (2021). Generation of a more efficient prime editor 2 by addition of the Rad51 DNA-binding domain. Nature Communications. 12(1). 5617–5617. 65 indexed citations
6.
Min, Seonwoo, et al.. (2021). Pre-Training of Deep Bidirectional Protein Sequence Representations With Structural Information. IEEE Access. 9. 123912–123926. 27 indexed citations
7.
Min, Seonwoo, et al.. (2021). Protein transfer learning improves identification of heat shock protein families. PLoS ONE. 16(5). e0251865–e0251865. 12 indexed citations
8.
Min, Seonwoo, Byunghan Lee, & Sungroh Yoon. (2021). TargetNet: functional microRNA target prediction with deep neural networks. Bioinformatics. 38(3). 671–677. 33 indexed citations
9.
Lim, Jung Min, Inkyung Jung, Seok‐Jae Heo, et al.. (2021). Recording of elapsed time and temporal information about biological events using Cas9. Cell. 184(4). 1047–1063.e23. 24 indexed citations
10.
Kim, Hui Kwon, Sungtae Lee, Younggwang Kim, et al.. (2020). Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. Nature Biotechnology. 38(9). 1037–1043. 81 indexed citations
11.
Kim, Nahye, Hui Kwon Kim, Sungtae Lee, et al.. (2020). Prediction of the sequence-specific cleavage activity of Cas9 variants. Nature Biotechnology. 38(11). 1328–1336. 146 indexed citations
12.
Kim, Hui Kwon, Goosang Yu, Jinman Park, et al.. (2020). Predicting the efficiency of prime editing guide RNAs in human cells. Nature Biotechnology. 39(2). 198–206. 193 indexed citations
13.
Kim, Hui Kwon, Sungtae Lee, Younggwang Kim, et al.. (2020). High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells. Nature Biomedical Engineering. 4(1). 111–124. 106 indexed citations
14.
Bae, Ho, Seonwoo Min, Hyun-Soo Choi, & Sungroh Yoon. (2020). DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(2). 888–898. 11 indexed citations
15.
Choi, Hyun-Soo, Seonwoo Min, Siwon Kim, et al.. (2019). Learning-Based Instantaneous Drowsiness Detection Using Wired and Wireless Electroencephalography. IEEE Access. 7. 146390–146402. 18 indexed citations
16.
Kim, Hui Kwon, Younggwang Kim, Sungtae Lee, et al.. (2019). SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance. Science Advances. 5(11). eaax9249–eaax9249. 140 indexed citations
17.
Kim, Hui Kwon, Seonwoo Min, Jae Woo Choi, et al.. (2018). Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nature Biotechnology. 36(3). 239–241. 245 indexed citations
18.
Park, Seunghyun, Seonwoo Min, Hyun-Soo Choi, & Sungroh Yoon. (2017). Deep Recurrent Neural Network-Based Identification of Precursor microRNAs. Neural Information Processing Systems. 30. 2891–2900. 22 indexed citations
19.
Hwang, Uiwon, et al.. (2017). A SeqGAN for Polyphonic Music Generation.. arXiv (Cornell University). 4 indexed citations
20.
Lee, Soo-Young & Seonwoo Min. (1991). Supervised Learning with multilayer Bidirectional Associative Memory.

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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