Author

Tsz-Lun Yeung

The University of Texas MD Anderson Cancer Center

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Bioinformatics· 1

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Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1

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Ángela Rynne‐VidaliD· 1 shared Christina S. LeslieiD· 1 shared Direna Alonso‐CurbeloiD· 1 shared Douglas A. LevineiD· 1 shared Fumiko ShimizuiD· 1 shared Gabriela ChiosisiD· 1 shared Hannah Yong WeniD· 1 shared Hatice U. OsmanbeyogluiD· 1 shared

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2019-2019 · peak 1/yr

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Ángela Rynne‐VidalORCID

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1 paper

Recent·Most cited

BioinformaticsNature CommunicationsPeer-reviewedSep 25, 2019

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Hatice U. Osmanbeyoglu, Fumiko Shimizu, Ángela Rynne‐Vidal…·Christina S. Leslie

The paper develops PSIONIC, a machine learning method that integrates chromatin accessibility and RNA-seq data to infer transcriptional programs in gynecologic and basal breast cancers, revealing regulatory differences and survival associations.

28 citations

humanATAC-seq