Seojin Bang

406 total citations
10 papers, 124 citations indexed

About

Seojin Bang is a scholar working on Molecular Biology, Artificial Intelligence and Physiology. According to data from OpenAlex, Seojin Bang has authored 10 papers receiving a total of 124 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Artificial Intelligence and 3 papers in Physiology. Recurrent topics in Seojin Bang's work include Software Engineering Research (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Seojin Bang is often cited by papers focused on Software Engineering Research (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Seojin Bang collaborates with scholars based in United States, United Kingdom and South Korea. Seojin Bang's co-authors include Heewook Lee, Pengfei Zhang, Chris Reed, Eduard Hovy, Yohan Jo, Wei Wu, Pengtao Xie, Eric P. Xing, Emaad Manzoor and Giuseppe Carenini and has published in prestigious journals such as PLoS ONE, Frontiers in Immunology and Pharmacogenetics and Genomics.

In The Last Decade

Seojin Bang

10 papers receiving 119 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seojin Bang United States	7	65	35	21	20	19	10	124
Tobias Frisch Denmark	7	61 0.9×	75 2.1×	10 0.5×	11 0.6×	22 1.2×	10	197
Sucheendra K. Palaniappan Japan	9	55 0.8×	62 1.8×	7 0.3×	4 0.2×	27 1.4×	18	176
C. Wolfe United States	4	52 0.8×	124 3.5×	11 0.5×	6 0.3×	6 0.3×	7	208
Raissa Relator Japan	5	69 1.1×	18 0.5×	20 1.0×	8 0.4×	3 0.2×	14	140
Shankai Yan Hong Kong	7	126 1.9×	133 3.8×	10 0.5×	22 1.1×	3 0.2×	25	246
Theofanis Karaletsos United States	7	51 0.8×	139 4.0×	10 0.5×	4 0.2×	7 0.4×	16	223
Farida Zehraoui France	10	67 1.0×	191 5.5×	17 0.8×	7 0.3×	6 0.3×	25	281
Todd Golub United States	3	41 0.6×	159 4.5×	5 0.2×	9 0.5×	9 0.5×	3	194
Siyuan Chen Saudi Arabia	7	36 0.6×	160 4.6×	5 0.2×	4 0.2×	33 1.7×	11	235
Manuel Martín-Merino Spain	6	31 0.5×	48 1.4×	4 0.2×	10 0.5×	3 0.2×	13	124

Countries citing papers authored by Seojin Bang

Since Specialization

Citations

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

Fields of papers citing papers by Seojin Bang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seojin Bang

This figure shows the co-authorship network connecting the top 25 collaborators of Seojin Bang. A scholar is included among the top collaborators of Seojin Bang 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 Seojin Bang. Seojin Bang 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:

10 of 10 papers shown

1.

Bang, Seojin, et al.. (2022). ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model. Frontiers in Immunology. 13. 893247–893247. 35 indexed citations

2.

Bar, Haim & Seojin Bang. (2021). A mixture model to detect edges in sparse co-expression graphs with an application for comparing breast cancer subtypes. PLoS ONE. 16(2). e0246945–e0246945. 2 indexed citations

3.

Bang, Seojin, Pengtao Xie, Heewook Lee, Wei Wu, & Eric P. Xing. (2021). Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach. Proceedings of the AAAI Conference on Artificial Intelligence. 35(13). 11396–11404. 31 indexed citations

4.

Jo, Yohan, Seojin Bang, Chris Reed, & Eduard Hovy. (2021). Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes. Transactions of the Association for Computational Linguistics. 9. 721–739. 16 indexed citations

5.

Jang, Hyeju, Seojin Bang, Wen Xiao, et al.. (2021). KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification. 96–107. 7 indexed citations

6.

Jo, Yohan, Seojin Bang, Emaad Manzoor, Eduard Hovy, & Chris Reed. (2020). Detecting Attackable Sentences in Arguments. 1–23. 14 indexed citations

7.

Bertholomey, Megan L., Kathryn L. Stone, TuKiet T. Lam, et al.. (2018). Phosphoproteomic Analysis of the Amygdala Response to Adolescent Glucocorticoid Exposure Reveals G-Protein Coupled Receptor Kinase 2 as a Target for Reducing Motivation for Alcohol. Proteomes. 6(4). 41–41. 3 indexed citations

8.

Hansen, Hanne D., Anders Hougaard, Seojin Bang, et al.. (2016). LOW 5-HT1B RECEPTOR BINDING IN THE MIGRAINE BRAIN:A PET STUDY. Research at the University of Copenhagen (University of Copenhagen). 6 indexed citations

9.

Bang, Seojin & Wei Wu. (2016). Naïve Bayes ensemble: A new approach to classifying unlabeled multi-class asthma subjects. 460–465. 1 indexed citations

10.

Kim, In‐Wha, Kyung Im Kim, Seojin Bang, et al.. (2012). Ethnic variability in the allelic distribution of pharmacogenes between Korean and other populations. Pharmacogenetics and Genomics. 22(12). 829–836. 9 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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