Author

Kai Silkwood

University of California, Irvine

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Arthur D. LanderiD· 2 shared Emmanuel DollingeriD· 2 shared Qing Hua NieiD· 2 shared Scott X. AtwoodiD· 2 shared Joshua Gervin· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersBioinformatics

Arthur D. LanderORCID

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2 papers

Recent·Most cited

Statistically principled feature selection for single cell transcriptomics

Emmanuel Dollinger, Kai Silkwood, Scott X. Atwood, Qing Hua Nie, Arthur D. Lander

The paper introduces a statistically principled feature selection method for scRNA-seq that improves accuracy in identifying rare cell types compared to existing tools.

BMC Bioinformatics · Oct 2, 2025Peer-reviewedBioinformatics

Leveraging gene correlations in single cell transcriptomic data

Kai Silkwood, Emmanuel Dollinger, Joshua Gervin, Scott X. Atwood, Qing Hua Nie, Arthur D. Lander

The paper introduces BigSur, a method for identifying gene-gene correlations in scRNA-seq data without normalization, showing it captures significant correlation structures and aligns with known biological processes.

BMC Bioinformatics · Sep 18, 2024Peer-reviewedSystems Biology