Y
Yue Joseph Wang
Researcher at Virginia Tech
Publications - 47
Citations - 629
Yue Joseph Wang is an academic researcher from Virginia Tech. The author has contributed to research in topics: Gene expression profiling & Visualization. The author has an hindex of 16, co-authored 47 publications receiving 599 citations. Previous affiliations of Yue Joseph Wang include Children's National Medical Center & The Catholic University of America.
Papers
More filters
Journal ArticleDOI
Identifying cancer biomarkers by network-constrained support vector machines
TL;DR: A network-based approach for cancer biomarker identification, netSVM, is developed, resulting in an improved prediction performance with network biomarkers and several novel hub genes, which may provide new insight to the underlying mechanism of breast cancer metastasis.
Journal ArticleDOI
UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples
Niya Wang,Ting Gong,Robert Clarke,Lulu Chen,Ie Ming Shih,Zhen Zhang,Douglas A. Levine,Jianhua Xuan,Yue Joseph Wang +8 more
TL;DR: A novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples, and results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly.
Journal ArticleDOI
DDN: a caBIG® analytical tool for differential network analysis.
Bai Zhang,Ye Tian,Lu Jin,Huai Li,Ie-Ming Shih,Subha Madhavan,Robert Clarke,Eric P. Hoffman,Jianhua Xuan,Leena Hilakivi-Clarke,Yue Joseph Wang +10 more
TL;DR: This work has developed a Cytoscape plug-in, CytoDDN, to integrate network analysis and visualization seamlessly and serves as a useful systems biology tool for users across biomedical research communities to infer how genetic, epigenetic or environment variables may affect biological networks and clinical phenotypes.
Journal ArticleDOI
BACOM: in silico detection of genomic deletion types and correction of normal cell contamination in copy number data.
TL;DR: A statistically principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately estimate genomic deletion type and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells is reported.
Journal ArticleDOI
Motif-directed network component analysis for regulatory network inference
TL;DR: In this article, a motif-directed NCA (mNCA) was proposed to integrate motif information and gene expression data to infer regulatory networks, which is applicable to many biological studies due to a lack of ChIP-on-chip data.