Author

Erin Kelley

Translational Genomics Research InstituteORCID 0000-0002-4164-0771Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Erin Kelley1 paper · Translational Genomics Research Institute;

Biology Disciplines

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Immunology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

John A. AltiniD· 3 shared Jorge Soria‐BustosiD· 3 shared Alexis Weston· 1 shared Caroline E. Harms· 1 shared Cristian TomasettiiD· 1 shared Ehsan Variani· 1 shared Erkut Hasan BorazanciiD· 1 shared Frank Yung-Chin TsaiiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersImmunology

John A. AltinORCID

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3 papers

Recent·Most cited

Preclinical Proof of Concept for a personalized SNAPTM-TIL (Specific Neo-Antigen Peptides -TIL) therapy platform

Tithi Ghosh, Erin Kelley, Jorge Soria‐Bustos, Trason G. Thode, Serina Ng, Taylor Bargenquast, Alexis Weston, Ryan Rodriguez del Villar, Mohan Rao Kaadige, Erkut Hasan Borazanci, Michael S. Gordon, Justin Moser, Frank Yung-Chin Tsai, Saul J. Priceman, Stephen J. Forman, , Sunil Sharma

This paper demonstrates a personalized SNAP-TIL therapy platform that improves TIL efficacy by enriching them with neoantigen-reactive T-cells, achieving high CD3+ cell content and significant tumor growth inhibition in preclinical models.

OncoImmunology · Jul 5, 2026Peer-reviewedImmunology

Learned Geometry, Predicted Binding: Structurally-BasedPrediction of Peptide:MHC Binding Using AlphaFold 3 EnablesCD4 T Cell Epitope Prediction

Kamel Lahouel, Mete Mulazimoglu, Jorge Soria‐Bustos, Kameron Bates, Erin Kelley, Lawson J Woods, Kunjur Manasa Upadhyaya, Gonzalo Raúl Acevedo, Sophie Pénisson, Matteo Munini, Ehsan Variani, Margaret E. Feeney, John A. Altin, Cristian Tomasetti

The paper introduces MHCIIFold-GNN, a method combining structural modeling and graph neural networks to predict CD4 T cell epitopes, achieving state-of-the-art performance.

bioRxiv · Jul 10, 2025 · PreprintBioinformatics

Integrated, high-dimensional analysis of CD4 T cell epitope specificities and phenotypes reveals unexpected diversity in the response to Mycobacterium tuberculosis

Heather Mead, Jung Hwa Kirschman, Caroline E. Harms, Erin Kelley, Jorge Soria‐Bustos, Georgia A. Nelson, Paul Ogongo, Gregory Sadat Ouma, Samuel Gurrion Ouma, J. Ernst, John A. Altin

The paper introduces CRESTA, a method to analyze CD4 T cell epitope specificities and phenotypes, revealing diverse responses to Mycobacterium tuberculosis.

bioRxiv · Nov 5, 2024 · PreprintImmunology