Author

Chenyang Dong

University of Wisconsin–MadisonORCID 0000-0002-1445-7154Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Developmental Biology· 1

Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 2 Assay:DNA methylation· 2 Research Focus:gene regulatory network inference· 2 Research Focus:GWAS / association mapping· 2 Organism:mouse· 2 Approach:statistical model· 2 Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Sündüz KeleşiD· 3 shared Alan AttieiD· 1 shared Anjelica RodrigueziD· 1 shared Chanul KimiD· 1 shared Donnie S. StapletoniD· 1 shared Fulai JiniD· 1 shared Gary A. ChurchilliD· 1 shared Isabella M. SilabaniD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

Sündüz KeleşORCID

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3 papers

Recent·Most cited

Harnessing the regenerative potential of interleukin11 to enhance heart repair

Kwangdeok Shin, Anjelica Rodriguez, Chanul Kim, Isabella M. Silaban, Yu Xia, Jisheng Sun, Chenyang Dong, Sündüz Keleş, Jinhu Wang, Jingli Cao, Junsu Kang

This study identifies IL11's dual role in heart regeneration and fibrosis, showing that combining IL11a induction with ERK inhibition can enhance regeneration while reducing fibrosis.

Nature Communications · Nov 8, 2024Peer-reviewedDevelopmental Biology

AdaLiftOver: High-resolution identification of orthologous regulatory elements with adaptive liftOver

Chenyang Dong, Siqi Shen, Sündüz Keleş

AdaLiftOver identifies orthologous regulatory elements across species by improving upon sequence conservation methods with epigenomic and sequence grammar features.

Bioinformatics · Mar 30, 2023Peer-reviewedBioinformatics

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Chenyang Dong, Shane P. Simonett, Sunyoung Shin, Donnie S. Stapleton, Kathryn L. Schueler, Gary A. Churchill, Leina Lu, Xiaoxiao Liu, Fulai Jin, Yan Li, Alan Attie, Mark P. Keller, Sündüz Keleş

INFIMA identifies effector genes of GWAS variants by integrating multi-omics data from mice and applying it to diabetes-associated variants.

Genome biology · Aug 23, 2021Peer-reviewedBioinformatics