Publications by authors named "Zhi-Hua Du"

Article Synopsis
  • * Although the overall incidence of SAH increased by 37.09% since 1990, age-standardized rates showed a decline, especially in high-income regions; mortality rates and disability-adjusted life years (DALYs) also decreased over time.
  • * The main demographic affected was those aged 50-69, with metabolic risk factors like high blood pressure being the leading contributors to SAH burden, while females generally experienced lower rates than males.
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Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data.

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Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task.

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Article Synopsis
  • The study focuses on gene regulation networks in humans, highlighting the complexity of interactions between transcription factors and target genes, with many interaction types still unconfirmed.
  • The authors introduce a new graph-based model called KGE-TGI that predicts these interactions using topology information rather than gene expression data.
  • Their method demonstrates high accuracy in predicting interaction types and link prediction tasks, achieving state-of-the-art performance and showcasing the importance of incorporating knowledge information in these predictions.
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Motivation: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones.

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The polymeric title compound, [Ce(C(7)H(3)N(2)O(6))(3)(C(2)H(6)OS)(2)](n), exists as a linear chain along [111] as the three dinitro-benzoate anions each engages in bridging adjacent dimethyl sulfoxide (DMSO) coordinated Ce(III) atoms. The metal atoms are surrounded by eight O atoms in a square-anti-prismatic environment. There are two independent formula units in the asymmetric unit.

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