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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.

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Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

TL;DR: This work takes the skeleton as the input at each time slot and introduces a novel regularization scheme to learn the co-occurrence features of skeleton joints, and proposes a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons.
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Errα and Gabpa/b specify PGC-1α-dependent oxidative phosphorylation gene expression that is altered in diabetic muscle

TL;DR: The results illustrate the dissection of gene regulatory networks in a complex mammalian system, elucidate the mechanism of PGC-1α action in the OXPHOS pathway, and suggest that Errα agonists may ameliorate insulin-resistance in individuals with type 2 diabetes mellitus.
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DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

TL;DR: The DanQ model, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence, improves considerably upon other models across several metrics.
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Distinguishing protein-coding and noncoding genes in the human genome

TL;DR: It is shown that the vast majority of nonconserved ORFs present by chance in RNA transcripts are random occurrences, and the results indicate that there has been relatively little true innovation in mammalian protein-coding genes.
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A Mammalian Organelle Map by Protein Correlation Profiling

TL;DR: This analysis ties biochemistry, cell biology, and genomics into a common framework for organelle analysis and identifies networks of coexpressed genes, cis-regulatory motifs, and putative transcriptional regulators involved in organelle biogenesis.