Author

Seonil Jee

Ghent University Global Campus

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:Arabidopsis· 3 Assay:DNA methylation· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:contrastive learning· 1 Approach:diffusion model· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Arnout Van MessemiD· 3 shared Espoir K. KabangaiD· 3 shared Wesley De NeveiD· 3 shared Soeun Yun· 1 shared Stephen DepuydtiD· 1 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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3 shared papersBioinformatics

Arnout Van MessemORCID

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3 papers

Recent·Most cited

Impact of U2-type introns on splice site prediction in Arabidopsis thaliana using deep learning

Espoir K. Kabanga, Seonil Jee, Soeun Yun, Stephen Depuydt, Arnout Van Messem, Wesley De Neve

This study uses deep learning to show that shorter and multiple U2-type introns improve splice site prediction in Arabidopsis thaliana.

BMC Bioinformatics · Nov 28, 2025Peer-reviewedGenomics

A DNA Sequence Imaging Approach to Predict Splice Sites Using Deep Learning

Espoir K. Kabanga, Seonil Jee, Arnout Van Messem, Wesley De Neve

This paper introduces a DNA sequence imaging method for splice site prediction, showing that DFCP outperforms FCGR using deep learning on Arabidopsis and human data.

bioRxiv · Jun 24, 2025 · PreprintBioinformatics

Frequency-Blended Diffusion Models for Synthetic Generation of Biologically Realistic Splice Site Sequences

Espoir K. Kabanga, Seonil Jee, Arnout Van Messem, Wesley De Neve

This paper introduces a frequency-blended diffusion model to generate biologically realistic splice site sequences, showing improved motif conservation and model transferability.

bioRxiv · Jun 16, 2025 · PreprintBioinformatics