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Xiang Wan
Researcher at The Chinese University of Hong Kong
Publications - 136
Citations - 11512
Xiang Wan is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 20, co-authored 100 publications receiving 6000 citations. Previous affiliations of Xiang Wan include University of British Columbia & Southwest Baptist University.
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Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range
TL;DR: In this article, the authors proposed a new estimation method by incorporating the sample size and compared the estimators of the sample mean and standard deviation under all three scenarios and presented some suggestions on which scenario is preferred in real-world applications.
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Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range
TL;DR: This work proposes a new estimation method by incorporating the sample size that greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data.
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Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range
TL;DR: This article investigates the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives and proposes estimators capable to serve as “rules of thumb” and will be widely applied in evidence-based medicine.
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Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model
Kimberly L. Stark,Bin Xu,Anindya Bagchi,Wen-Sung Lai,Hui Liu,Ruby Hsu,Xiang Wan,Paul Pavlidis,Alea A. Mills,Maria Karayiorgou,Joseph A. Gogos +10 more
TL;DR: Evidence is provided that the abnormal miRNA biogenesis emerges because of haploinsufficiency of the Dgcr8 gene, which encodes an RNA-binding moiety of the 'microprocessor' complex and contributes to the behavioral and neuronal deficits associated with the 22q11.2 microdeletion.
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BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies
TL;DR: BOOST has identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set and can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.