Author

Kun Xiong

University of ArizonaORCID 0000-0003-1431-6586Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Kun Xiong1 paper · Regeneron (United States)

Biology Disciplines

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Genomics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Approach:simulation· 1

Frequent Co-Authors

Aleksandar MilosavljeviciD· 1 shared Alex K. LancasteriD· 1 shared Alexander DobiniD· 1 shared Alexandra Scavelli· 1 shared Ali MortazaviiD· 1 shared Ana BertheliD· 1 shared Anna V VlasovaiD· 1 shared B BernsteiniD· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperGenomics

Aleksandar MilosavljevicORCID

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2 papers

Recent·Most cited

GenomicsCellPeer-reviewedMar 1, 2023

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Joel S. Rozowsky, Jiahao Gao, Beatrice Borsari…·B Bernstein

The paper introduces the EN-TEx resource, which provides personal epigenomes and variant-impact models across multiple tissues, revealing allele-specific loci and their associations with GWAS loci.

78 citationstop 1%

humanDNA methylation

𝕏 PostBluesky

Systems BiologyNature CommunicationsPeer-reviewedJun 3, 2019

Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise

Kun Xiong, Alex K. Lancaster, Mark L. Siegal, Joanna Masel

The paper shows that feed-forward loops and diamond motifs evolve as signal filters, with dynamics rather than topology being key under intrinsic noise.

17 citations

statistical modelsimulation

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