Author

Timothy Kunz

Harvard UniversityORCID 0000-0002-4162-1132Unclaimed 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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Organism:human· 2 Approach:statistical model· 2 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Assay:Hi-C· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

José Rivera‐FelicianoiD· 1 shared Lila Rieber· 1 shared Shaun MahonyiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

3 top collaborators · 1 mutual co-author link

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1 shared paperBioinformatics

José Rivera‐FelicianoORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintFeb 2, 2026

DBSOMA: A Machine Learning Method that Identifies Chemical Modulators of Transcriptional States Uncovers Effectors of Beta-Cell Maturation

Timothy Kunz, José Rivera‐Feliciano

DBSOMA identifies chemical modulators of transcriptional states in beta-cell maturation using single-cell RNA-seq data and clustering analysis.

DOI 10.64898/2026.01.30.701818

𝕏 PostBluesky

BioinformaticsMethodsPeer-reviewedJul 8, 2020

Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing map

Timothy Kunz, Lila Rieber, Shaun Mahony

The paper introduces a SOM-based method to visualize and analyze relationships between chromatin interactions and regulatory genomic activities using Hi-C data.

6 citations

𝕏 PostBluesky