Seokjun Seo

860 total citations
9 papers, 457 citations indexed

About

Seokjun Seo is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Seokjun Seo has authored 9 papers receiving a total of 457 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Artificial Intelligence and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Seokjun Seo's work include Bioinformatics and Genomic Networks (3 papers), Gene expression and cancer classification (3 papers) and Speech and dialogue systems (2 papers). Seokjun Seo is often cited by papers focused on Bioinformatics and Genomic Networks (3 papers), Gene expression and cancer classification (3 papers) and Speech and dialogue systems (2 papers). Seokjun Seo collaborates with scholars based in South Korea and United States. Seokjun Seo's co-authors include Beomsu Kim, Sungjoo Ha, Dongyoung Kim, Buru Chang, Seungju Han, Sun Kim, Kwanghee Choi, Youngjun Park, Minsik Oh and Seungwoo Choi and has published in prestigious journals such as Bioinformatics, Methods and BMC Systems Biology.

In The Last Decade

Seokjun Seo

9 papers receiving 437 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seokjun Seo South Korea	6	214	168	100	86	29	9	457
Zafer Aydın Türkiye	13	162 0.8×	64 0.4×	209 2.1×	28 0.3×	73 2.5×	52	516
Shah Nawaz Pakistan	11	134 0.6×	173 1.0×	11 0.1×	60 0.7×	18 0.6×	44	330
Haixia Long China	9	74 0.3×	49 0.3×	71 0.7×	17 0.2×	66 2.3×	24	260
Shaohong Zhang China	9	101 0.5×	64 0.4×	122 1.2×	21 0.2×	7 0.2×	38	286
Chunjie Wang China	4	185 0.9×	69 0.4×	51 0.5×	14 0.2×	8 0.3×	11	296
Naiyang Guan China	5	62 0.3×	218 1.3×	39 0.4×	37 0.4×	8 0.3×	12	318
Mustafa M. Abd Elnaby Egypt	12	98 0.5×	86 0.5×	36 0.4×	45 0.5×	11 0.4×	34	297
Mohamed Ishmael Belghazi Canada	3	228 1.1×	106 0.6×	15 0.1×	47 0.5×	18 0.6×	5	309
R. Cristin India	9	88 0.4×	79 0.5×	18 0.2×	24 0.3×	16 0.6×	31	279
Baoru Han China	10	63 0.3×	196 1.2×	14 0.1×	28 0.3×	24 0.8×	39	330

Countries citing papers authored by Seokjun Seo

Since Specialization

Citations

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

Fields of papers citing papers by Seokjun Seo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seokjun Seo

This figure shows the co-authorship network connecting the top 25 collaborators of Seokjun Seo. A scholar is included among the top collaborators of Seokjun Seo 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 Seokjun Seo. Seokjun Seo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Han, Seungju, Beomsu Kim, Jin Yong Yoo, et al.. (2022). Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5114–5132. 9 indexed citations

2.

Han, Seungju, et al.. (2022). Understanding and Improving the Exemplar-based Generation for Open-domain Conversation. 218–230. 2 indexed citations

3.

Han, Seungju, et al.. (2021). Disentangling Label Distribution for Long-tailed Visual Recognition. 6622–6632. 143 indexed citations

4.

Ha, Sungjoo, et al.. (2020). MarioNETte: Few-Shot Face Reenactment Preserving Identity of Unseen Targets. Proceedings of the AAAI Conference on Artificial Intelligence. 34(7). 10893–10900. 96 indexed citations

5.

Choi, Seungwoo, et al.. (2019). Temporal Convolution for Real-Time Keyword Spotting on Mobile Devices. 3372–3376. 100 indexed citations

6.

Seo, Seokjun, Minsik Oh, Youngjun Park, & Sun Kim. (2018). DeepFam: deep learning based alignment-free method for protein family modeling and prediction. Bioinformatics. 34(13). i254–i262. 91 indexed citations

7.

Lim, Sangsoo, et al.. (2017). PINTnet: construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI. BMC Systems Biology. 11(S2). 15–15. 12 indexed citations

8.

Chae, Heejoon, et al.. (2016). BioVLAB-mCpG-SNP- EXPRESS : A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data. Methods. 111. 64–71. 3 indexed citations

9.

Hur, Benjamin, Sangsoo Lim, Heejoon Chae, et al.. (2016). CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments. Biology Direct. 11(1). 57–57. 1 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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