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Xiang Zhang

Researcher at Baylor College of Medicine

Publications -  3483
Citations -  144843

Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.

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Cancer-associated fibroblast-induced lncRNA UPK1A-AS1 confers platinum resistance in pancreatic cancer via efficient double-strand break repair

TL;DR: Wang et al. as mentioned in this paper found that UPK1A-AS1, whose expression is directly induced by IL8/NF-kappa B signaling, functions as a chemoresistance-promoting lncRNA and is critical for active IL8-induced oxaliplatin resistance.
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Zircon U-Pb dating of the metamorphic rocks of different grades from the southern part of the Dabie terrain in China

TL;DR: In this article, the ages of regional orthogneisses and enclosed phyllite in South Dabie unit as well as schist and amphibolite in Susong metamorphic belt were investigated.
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Unique cellular protrusions mediate breast cancer cell migration by tethering to osteogenic cells.

TL;DR: An alternative “migration-by-tethering” mechanism through which cancer cells gain the momentum to migrate by adhering to mesenchymal stem cells or osteoblasts is discovered, which exemplify how cancer cells may acquire migratory ability without intrinsic reprogramming.
Proceedings ArticleDOI

Exciton-related electroluminescence from monolayer MoS2

TL;DR: In this paper, the microscopic origin of electroluminescence from monolayer MoS2 fabricated on a heavily p-type doped silicon substrate was studied, and the Auger recombination of the exciton-exciton annihilation of bound exciton emission was observed.
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ComClus: A Self-Grouping Framework for Multi-Network Clustering

TL;DR: ComClus is novel in combining the clustering approach of non-negative matrix factorization (NMF) and the feature subspace learning approach of metric learning and can effectively leverage prior knowledge on how to group networks such that network grouping can be conducted in a semi-supervised manner.