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Jun Liu

Researcher at Sun Yat-sen University

Publications -  1474
Citations -  92168

Jun Liu is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 100, co-authored 1165 publications receiving 73692 citations. Previous affiliations of Jun Liu include Shanghai Jiao Tong University & Genome Institute of Singapore.

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Journal ArticleDOI

Sequential Imputations and Bayesian Missing Data Problems

TL;DR: This article introduces an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights, and in many applications this new procedure works very well without the need for iterations.
Proceedings ArticleDOI

BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes.

TL;DR: BioProspector, a C program using a Gibbs sampling strategy, examines the upstream region of genes in the same gene expression pattern group and looks for regulatory sequence motifs, showing preliminary success in finding the binding motifs for Saccharomyces cerevisiae RAP1, Bacillus subtilis RNA polymerase, and Escherichia coli CRP.
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Guillain-Barré syndrome associated with SARS-CoV-2 infection: causality or coincidence?

TL;DR: The patient’s initial labor abnorma lities, which were consistent with clinical characteristics of patients with COVID-19, indicated the presence of SARS-CoV-2 infection on admission, and it is considered that the virus was transmitted to her relatives dur ing her hospital stay.
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Methylation of histone H3 Lys 4 in coding regions of active genes.

TL;DR: It is concluded that Set1 facilitates transcription, in part, by protecting active coding regions from deacetylation, in the context of recent studies showing that Lys 4 methylation precludes histone de acetylase recruitment.
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An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments.

TL;DR: MDscan correctly identified all the experimentally verified motifs from published ChIP–array experiments in yeast, and predicted two motif patterns for the differential binding of Rap1 protein in telomere regions, and was faster and more accurate than several established motif-finding algorithms.