Author

Jack M. Shireman

University of Wisconsin–MadisonORCID 0000-0002-5088-7805Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:large language model· 2 Assay:scRNA-seq· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1

Frequent Co-Authors

Christina M. KendziorskiiD· 3 shared Lingxin ChengiD· 3 shared Yujia CaiiD· 3 shared Elliot XieiD· 2 shared Mahua DeyiD· 2 shared Akash GOGATE· 1 shared Chitrasen MohantyiD· 1 shared Jihua LiuiD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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3 shared papersBioinformatics

Christina M. KendziorskiORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintOct 29, 2026

Agentic Discovery Transfer Enables Cross-Cancer Discovery at Scale

Lingxin Cheng, Jack M. Shireman, Akash GOGATE…·Christina M. Kendziorski

SMART, a multi-agent framework, transfers findings from single-cell RNA-sequencing cancer studies to other cancer types, identifying cross-cancer prognostic signatures and generating new hypotheses.

DOI 10.64898/2026.10.28.762506

humanscRNA-seq

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BioinformaticsbioRxivPreprintJun 16, 2026

AutoZyme: An Autonomous Agentic Framework to Optimize Bioinformatics Software

Elliot Xie, Lingxin Cheng, Yujia Cai…·Christina M. Kendziorski

AutoZyme optimizes bioinformatics software by autonomously identifying and applying code changes to improve runtime without significant memory use.

DOI 10.64898/2026.06.12.731250

experimental assaybenchmarking

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BioinformaticsbioRxivPreprintDec 4, 2024

CASSIA: a multi-agent large language model for reference free, interpretable, and automated cell annotation of single-cell RNA-sequencing data

Elliot Xie, Lingxin Cheng, Jack M. Shireman…·Christina M. Kendziorski

CASSIA automates and improves cell type annotation in single-cell RNA-sequencing data with interpretability and accuracy.

3 citations

scRNA-seqlarge language model

𝕏 PostBluesky