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Yin Tang on Muck Rack

Yin Tang

As seen in: Color Research & Application, Nature, Wiley Online Library, Frontiers, MDPI, Taylor & Francis Online, BMC Cancer, Royal Society of Chemistry, Cell Press, Physical Review B and
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Articles

Wnt-presenting materials sustain H3K14-acetylation in human skeletal stem cells for tissue engineering and bone repair

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By Pierre Becquart, Pierre Tournier, Sergi Junyent, Binbin Ma, Yu Chi Liu, Yin Tang, Xin McRae Chen, Dong Fang, Anjali P. Kusumbe, Shukry J. Habib
| Nature Verified
Abstract Engineering functional tissues for transplantation requires insight into epigenetic mechanisms that regulate stem cell fate. We have developed the Wnt-induced osteogenic tissue model (WIOTM), a platform that recapitulates human osteogenesis, and identified acetylation of histone H3 at lysine 14 (H3K14ac) as a critical epigenetic regulator in human skeletal stem cells (hSSCs).
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Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction

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By Yin Tang
| Taylor & Francis Online
Advanced search Journal of the American Statistical Association Latest Articles Submit an article Journal homepage Full Article Figures & data References Supplemental Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/01621459.2025.2590775?needAccess=true Accepted author version Abstract We introduce a unified, flexible, and easy-to-implement framework of sufficient dimension reduction that can accommodate both linear and nonlinear dimension reduction, and both the...
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A KL-divergence-based test for elliptical distribution

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By Yin Tang, Yanyuan Ma
| Taylor & Francis Online
We conduct a KL-divergence-based procedure for testing elliptical distributions. The procedure simultaneously takes into account the two defining properties of an elliptically distributed random vector: independence between length and direction, and uniform distribution of the direction. The test statistic is constructed based on the k nearest neighbours (kNN) method, and two cases are considered where the mean vector and covariance matrix are known and unknown.
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