D
Dongjun Chung
Researcher at Ohio State University
Publications - 99
Citations - 1961
Dongjun Chung is an academic researcher from Ohio State University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 18, co-authored 76 publications receiving 1443 citations. Previous affiliations of Dongjun Chung include University of Wisconsin-Madison & Yale University.
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Journal ArticleDOI
Metabolic Regulation of Gene Expression by Histone Lysine β-Hydroxybutyrylation
Zhongyu Xie,Di Zhang,Dongjun Chung,Dongjun Chung,Zhanyun Tang,He Huang,Lunzhi Dai,Shankang Qi,Jing-Ya Li,Gozde Colak,Yue Chen,Chunmei Xia,Chao Peng,Hai Bin Ruan,Matt Kirkey,Dan-Li Wang,Lindy Jensen,Oh Kwang Kwon,Sangkyu Lee,Scott D. Pletcher,Minjia Tan,David B. Lombard,Kevin P. White,Hongyu Zhao,Jia Li,Robert G. Roeder,Xiaoyong Yang,Yingming Zhao +27 more
TL;DR: Histone Kbhb represents a new epigenetic regulatory mark that couples metabolism to gene expression, offering a new avenue to study chromatin regulation and diverse functions of β-hydroxybutyrate in the context of important human pathophysiological states, including diabetes, epilepsy, and neoplasia.
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GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.
TL;DR: A novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information and to jointly analyze five psychiatric disorders with annotation information.
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Sparse partial least squares classification for high dimensional data.
Dongjun Chung,Sunduz Keles +1 more
TL;DR: This work develops sparse versions of the recently proposed two PLS-based classification methods using sparse partial least squares (SPLS), and shows that incorporation of SPLS into a generalized linear model (GLM) framework provides higher sensitivity in variable selection for multicategory classification with unbalanced sample sizes between classes.
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Genome-scale analysis of escherichia coli FNR reveals complex features of transcription factor binding.
Kevin S. Myers,Huihuang Yan,Irene M. Ong,Dongjun Chung,Kun Liang,Frances Tran,Sunduz Keles,Robert Landick,Robert Landick,Patricia J. Kiley,Patricia J. Kiley +10 more
TL;DR: It is found that FNR occupancy at many target sites is strongly influenced by nucleoid-associated proteins (NAPs) that restrict access to many FNR binding sites, and genome accessibility may also explain the finding that genome-wide F NR occupancy did not correlate with the match to consensus at binding sites.
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A Statistical Framework for the Analysis of ChIP-Seq Data
TL;DR: A background model that accounts for apparent sources of biases such as mappability and GC content is introduced and a flexible mixture model named MOSAiCS for detecting peaks in both one- and two-sample analyses of ChIP-Seq data is developed.