Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Lingzhou Xue"'
Autor:
Misbahud Din, Souvik Paul, Sana Ullah, Haoyi Yang, Rong-Guang Xu, Nurul Aisha Zainal Abidin, Allan Sun, Yiyao Catherine Chen, Rui Gao, Bari Chowdhury, Fangyuan Zhou, Stephenie Rogers, Mariel Miller, Atreyee Biswas, Liang Hu, Zhichao Fan, Christopher Zahner, Jing Fan, Zi Chen, Megan Berman, Lingzhou Xue, Lining Arnold Ju, Yunfeng Chen
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Abstract Arterial thrombosis is a leading cause of death and disability worldwide with no effective bioassay for clinical prediction. As a symbolic feature of arterial thrombosis, severe stenosis in the blood vessel creates a high-shear, high-gradien
Externí odkaz:
https://doaj.org/article/12aa995c0a4640e6839f160fbc60cfa4
Publikováno v:
Statistics and Public Policy, Vol 8, Iss 1, Pp 73-79 (2021)
Government reports document more than 14,000 homicides and more than 195,000 aggravated assaults with firearms in 2017. In addition, there were 346 mass shootings, with 4 or more victims, including over 2000 people shot. These statistics do not inclu
Externí odkaz:
https://doaj.org/article/a977cbda58d64d1980e12987e7d28c90
Autor:
Kalins Banerjee, Ni Zhao, Arun Srinivasan, Lingzhou Xue, Steven D. Hicks, Frank A. Middleton, Rongling Wu, Xiang Zhan
Publikováno v:
Frontiers in Genetics, Vol 10 (2019)
Differential abundance analysis is a crucial task in many microbiome studies, where the central goal is to identify microbiome taxa associated with certain biological or clinical conditions. There are two different modes of microbiome differential ab
Externí odkaz:
https://doaj.org/article/b93ab476c61d4244890a35b0b8ee4237
Autor:
Yafei Lyu, Lingzhou Xue, Feipeng Zhang, Hillary Koch, Laura Saba, Katerina Kechris, Qunhua Li
Publikováno v:
PLoS Computational Biology, Vol 14, Iss 9, p e1006436 (2018)
Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particu
Externí odkaz:
https://doaj.org/article/2d8de1348e204b88b79377b0b5519821
Publikováno v:
eLife, Vol 5 (2016)
How cells sense their mechanical environment and transduce forces into biochemical signals is a crucial yet unresolved question in mechanobiology. Platelets use receptor glycoprotein Ib (GPIb), specifically its α subunit (GPIbα), to signal as they
Externí odkaz:
https://doaj.org/article/0facf10e6d57497caa211e9217b70e9d
Publikováno v:
Statistica Sinica.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29a913a946f96b1e2a8c80162d01609c
https://doi.org/10.5705/ss.202021.0147
https://doi.org/10.5705/ss.202021.0147
Publikováno v:
Journal of the American Statistical Association. :1-14
Testing large covariance matrices is of fundamental importance in statistical analysis with high-dimensional data. In the past decade, three types of test statistics have been studied in the literature: quadratic form statistics, maximum form statist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3d0d4d418ffb8ea748b9b0d007ef4b0
https://doi.org/10.1080/01621459.2022.2126781
https://doi.org/10.1080/01621459.2022.2126781
Publikováno v:
Journal of the American Statistical Association. :1-14
We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three way statistical relation that shares similar properties with conditional independence by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ef3e5386ae2c175d100cb42b38cbb1d
https://doi.org/10.1080/01621459.2022.2119983
https://doi.org/10.1080/01621459.2022.2119983
Publikováno v:
Journal of Business & Economic Statistics. :1-11
Compositional data arises in a wide variety of research areas when some form of standardization and composition is necessary. Estimating covariance matrices is of fundamental importance for high-dimensional compositional data analysis. However, exist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f7b573d3db8edb1d8bf4b8442e7e6c1
https://doi.org/10.1080/07350015.2022.2106990
https://doi.org/10.1080/07350015.2022.2106990
Publikováno v:
INFORMS Journal on Optimization. 4:200-214
Spectral clustering is one of the fundamental unsupervised learning methods and is widely used in data analysis. Sparse spectral clustering (SSC) imposes sparsity to the spectral clustering, and it improves the interpretability of the model. One wide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::997f2ee349d95aa05011252e9b4806b7
https://doi.org/10.1287/ijoo.2021.0064
https://doi.org/10.1287/ijoo.2021.0064