Author

Kelly Patrick Stanton

Yale Cancer CenterORCID 0000-0002-0707-5457Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:ChIP-seq· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Organism:human· 1 Assay:multiome· 1 Approach:statistical model· 1

Frequent Co-Authors

Yuval KlugeriD· 2 shared Huamin LiiD· 1 shared Jiaqi Jin· 1 shared Ruth R. MontgomeryiD· 1 shared Sherman Weissman· 1 shared Uri ShahamiD· 1 shared Yi YaoiD· 1 shared

Activity by Year

2015-2017 · peak 1/yr

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7 top collaborators · 13 mutual co-author links

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2 shared papersBioinformatics

Yuval KlugerORCID

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2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedJul 7, 2017

Gating mass cytometry data by deep learning

Huamin Li, Uri Shaham, Kelly Patrick Stanton…·Yuval Kluger

The paper introduces DeepCyTOF, a deep learning method for automating cell gating in mass cytometry data, achieving high concordance with manual gating and performing well on benchmark challenges.

109 citations

humanmultiome

𝕏 PostBluesky

GenomicsbioRxivPreprintDec 11, 2015

Ritornello: High fidelity control-free chip-seq peak calling

Kelly Patrick Stanton, Jiaqi Jin, Sherman Weissman, Yuval Kluger

Ritornello improves ChIP-seq peak calling by eliminating the need for a total DNA control, reducing costs and improving accuracy over existing methods.

DOI 10.1101/034090

ChIP-seqstatistical model

𝕏 PostBluesky