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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.

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Proceedings ArticleDOI

Surface reconstruction and visualization of the surgical prostate model

TL;DR: An advanced image analysis and graphics software is developed to reconstruct and visualize previously images prostate specimens to define tumor volume and distribution and pathways of needle biopsies, thus allowing improved understanding of prostate cancer behavior and current diagnosis-staging methodology.
Journal ArticleDOI

Knowledge-guided multi-scale independent component analysis for biomarker identification

TL;DR: The results show that the knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification and shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.
Proceedings ArticleDOI

Tactile mapping of palpable abnormalities for breast cancer diagnosis

TL;DR: A prototype tactile mapping device (TMD) system comprised mainly of a tactile sensor array probe, a 3D camera and a force/torque sensor which can provide the means to produce tactile maps of the breast lumps during a breast palpation is presented.
Proceedings ArticleDOI

Inference of Gene Regulatory Networks from Time Course Gene Expression Data Using Neural Networks and Swarm Intelligence

TL;DR: A novel algorithm that combines a recurrent neural network (RNN) and two swarm intelligence (SI) methods to infer a gene regulatory network (GRN) from time course gene expression data is presented.
Journal ArticleDOI

Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards.

TL;DR: It is demonstrated that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information.