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Xiaohui Xie
Researcher at University of California, Irvine
Publications - 351
Citations - 34195
Xiaohui Xie is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 58, co-authored 220 publications receiving 29844 citations. Previous affiliations of Xiaohui Xie include University of California, Berkeley & National Chiao Tung University.
Papers
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Journal ArticleDOI
Developmentally regulated long non-coding RNAs in Xenopus tropicalis
Elmira Forouzmand,Nick D.L. Owens,Ira L. Blitz,Kitt D. Paraiso,Mustafa K. Khokha,Michael J. Gilchrist,Xiaohui Xie,Ken W.Y. Cho +7 more
TL;DR: The results suggest that lncRNAs have regulatory roles during early embryonic development, and a subset of them displays highly correlative temporal expression profiles with respect to those of the neighboring genes.
Journal ArticleDOI
MixClone: a mixture model for inferring tumor subclonal populations
Yi Li,Xiaohui Xie +1 more
TL;DR: A novel probabilistic mixture model, MixClone, is described for inferring the cellular prevalences of subclonal populations directly from whole genome sequencing of paired normal-tumor samples and it is shown that integrating sequence information from both somatic copy number alterations and allele frequencies can significantly improve the accuracy of inferring tumor subClonal populations.
Journal ArticleDOI
Development and external validation of a prognostic tool for COVID-19 critical disease.
Daniel S. Chow,Justin Glavis-Bloom,Jennifer E. Soun,Brent D. Weinberg,Theresa B Loveless,Xiaohui Xie,Simukayi Mutasa,Edwin S. Monuki,Jung In Park,Daniela A. Bota,Jie Wu,Leslie M. Thompson,Bernadette Boden-Albala,Saahir Khan,Alpesh Amin,Peter D. Chang +15 more
TL;DR: A model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values is presented, on derivation and validation cohorts from two different institutions.
Posted Content
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
TL;DR: Li et al. as mentioned in this paper proposed an end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning.
Posted Content
Learning sparse gradients for variable selection and dimension reduction
Gui-Bo Ye,Xiaohui Xie +1 more
TL;DR: In this article, an integrated approach, called sparse gradient learning (SGL), is proposed for variable selection and dimension reduction via learning the gradients of the prediction function directly from samples.