Author

Seungjun Ahn

Icahn School of Medicine at Mount SinaiORCID 0000-0002-4816-8924Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

Eun Jeong OhiD· 2 shared Ali ShojaieiD· 1 shared Diddier PradaiD· 1 shared Ethan Park· 1 shared Hannah Lee· 1 shared Tristan ThamiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 9 mutual co-author links

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2 shared papersBioinformatics

Eun Jeong OhORCID

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2 papers

Recent·Most cited

Integrating Spatially Adjusted Protein Summaries for Survival Prediction in Spatial Proteomics

Seungjun Ahn, Eun Jeong Oh, Diddier Prada, Ali Shojaie

This paper introduces a framework to improve survival prediction in spatial proteomics by incorporating spatially adjusted protein summaries into Cox models, showing better performance in simulations and applications to breast cancer data.

bioRxiv · Sep 4, 2026 · PreprintBioinformatics

Super Learner Ensemble Modeling of CPTAC Proteomic Data for Survival Prediction in Head and Neck Squamous Cell Carcinoma

Ethan Park, Hannah Lee, Eun Jeong Oh, Tristan Tham, Seungjun Ahn

This study uses Super Learner ensemble modeling on CPTAC proteomic data to predict survival in HNSCC, finding that the ensemble method performs stably with high C-index values.

bioRxiv · Jun 16, 2026 · PreprintBioinformatics