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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

Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle

TL;DR: A Bayesian model is introduced to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast and a novel Metropolis-Hastings group move is developed in order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution.
Journal ArticleDOI

Variants in the SNCA Locus Are Associated With the Progression of Parkinson's Disease.

TL;DR: The results indicated that genetic variation in SNCA may contribute to variability natural progression of PD and could possibly be used as a prognostic marker.
Book ChapterDOI

Synthetic mRNA Reprogramming of Human Fibroblast Cells.

TL;DR: To facilitate the translation of iPSC technology to clinical practice, mRNA reprogramming method that generates transgene-free iPSCs is a safe and efficient method, eliminating bio-containment concerns associated with viral vectors, as well as the need for weeks of screening of cells to confirm that viral material has been completely eliminated during cell passaging.
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Establishment and characterization of fetal fibroblast cell lines for generating human lysozyme transgenic goats by somatic cell nuclear transfer

TL;DR: The lifespan of GFF cell lines has a major effect on the efficiency to produce transgenic cloned goats, and the proliferative lifespan of primary cells may be used as a criterion to characterize the quality of cell lines for genetic modification and SCNT.
Journal ArticleDOI

A hotspots analysis-relation discovery representation model for revealing diabetes mellitus and obesity.

TL;DR: A novel model named as representative latent Dirichlet allocation (RLDA) topic model was applied to a corpus of more than 337,000 literatures of diabetes and obesity and extracted the significant relationships between them and other diseases such as Alzheimer’s disease, heart disease and tumor.