Author

Kairi Tanaka

University of California San DiegoORCID 0009-0004-1575-2587Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Cancer Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Ajay SubramanianiD· 1 shared Everett James ModingiD· 1 shared Hannah K. CarteriD· 1 shared Ko-Han LeeiD· 1 shared María Muñoz PéreziD· 1 shared Maurizio ZanettiiD· 1 shared Meghana S. PagadalaiD· 1 shared Robert Rasmussen· 1 shared

Activity by Year

2026-2026 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 36 mutual co-author links

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1 shared paperCancer Biology

Ajay SubramanianORCID

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1 paper

Recent·Most cited

Predicting Neoantigen Immunogenicity from In Vivo Immune Editing

Timothy J. Sears, Ko-Han Lee, María Muñoz Pérez, Robert Rasmussen, Meghana S. Pagadala, Kairi Tanaka, Ajay Subramanian, Everett James Moding, Maurizio Zanetti, Hannah K. Carter

This paper introduces NEMo, a machine learning model that predicts neoantigen immunogenicity using in vivo immune editing data, showing improved stratification of progression-free survival across multiple clinical settings.

bioRxiv · Sep 18, 2026 · PreprintCancer Biology