Author

Daniel J Carulli

University of MissouriORCID 0000-0002-6336-7008Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1 Assay:proteomics· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Akshata HegdeiD· 1 shared Frimpong BoaduiD· 1 shared Jianlin ChengiD· 1 shared Ryan M FinnertyiD· 1 shared Sarayut WinuthayanoniD· 1 shared Wipawee WinuthayanoniD· 1 shared Yanli WangiD· 1 shared

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2024-2024 · peak 1/yr

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7 top collaborators · 21 mutual co-author links

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1 shared paperCell Biology

Akshata HegdeORCID

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1 paper

Recent·Most cited

Cell BiologyeLifePeer-reviewedOct 1, 2024

Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos

Ryan M Finnerty, Daniel J Carulli, Akshata Hegde…·Wipawee Winuthayanon

The paper uses multi-omics data and machine learning to analyze oviductal responses to gametes and embryos in mice, identifying region-specific transcriptional and proteomic changes.

18 citationstop 4%

mousebulk RNA-seq

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