Y
Yang Hu
Researcher at Harbin Institute of Technology
Publications - 306
Citations - 6858
Yang Hu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Medicine & Genome-wide association study. The author has an hindex of 35, co-authored 258 publications receiving 4437 citations. Previous affiliations of Yang Hu include Shanghai International Studies University & Cornell University.
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Aging of poly(lactide)/poly(ethylene glycol) blends. Part 2. Poly(lactide) with high stereoregularity
TL;DR: In this article, a mixture of poly(lactide and polyethylene glycol (PEG) was used to improve the mechanical properties of a PLA with low stereoregularity.
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Lateral organ boundaries 1 is a disease susceptibility gene for citrus bacterial canker disease
Yang Hu,Junli Zhang,Hongge Jia,Davide Sosso,Ting Li,Wolf-Bernd Frommer,Bing Yang,Frank F. White,Nian Wang,Jeffrey B. Jones +9 more
TL;DR: The results indicate that CBC-inciting species of Xanthomonas exploit a single host disease susceptibility gene by altering the expression of an otherwise developmentally regulated gene using any one of a diverse set of TAL effector genes in the pathogen populations.
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Crystallization and phase separation in blends of high stereoregular poly(lactide) with poly(ethylene glycol)
TL;DR: The effect of cooling rate on crystallization and subsequent aging of high stereoregular poly(lactide) (PLA) blended with poly(ethylene glycol) (PEG) was studied by thermal analysis and by direct observation of the solid state structure with atomic force microscopy (AFM) as discussed by the authors.
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DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function
TL;DR: DincRNA is a comprehensive web-based bioinformatics toolkit to elucidate the entangled relationships among diseases and non-coding RNAs (ncRNAs) from the perspective of disease similarity, and implemented all of the above eight algorithms based on DO and disease-related genes.
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Identifying drug–target interactions based on graph convolutional network and deep neural network
TL;DR: A novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification that first uses a graph convolved network to learn the features for each DPP, and uses a deep neural network to predict the final label.