Publications by authors named "Xing Feiyang"

Article Synopsis
  • - Cells are the basic building blocks of life, each with distinct growth patterns and molecular characteristics, which are influenced by their genomes.
  • - Recent advancements in single-cell sequencing technologies have broadened to cover various cellular components like genomes, epigenomes, and proteomes, and have started integrating diverse data types, enhancing our understanding of diseases like cancer.
  • - This text reviews new developments in single-cell omics technologies and focuses on methodologies, providing guidance for researchers in choosing the right approaches for single-cell sequencing and data analysis.
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Article Synopsis
  • Graph learning models are gaining popularity for analyzing single-cell RNA sequencing (scRNA-seq) data, outperforming traditional deep neural networks by extracting graph-structured data from gene count matrices.
  • Unlike conventional clustering methods that focus on temporal expression patterns, this study emphasizes both genetic and cellular interactions, viewing them as essential for understanding spatial dynamics in single-cell data.
  • The study introduces the scHybridBERT architecture, utilizing multi-view modeling to incorporate spatiotemporal patterns, and demonstrates significant improvements in cell type detection accuracy through experimental tests on benchmark datasets.
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Background: The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment.

Methods: Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes.

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Gastric cancer has two distinct subtypes: the diffuse (DGC) and the intestinal (IGC) subtypes. Morphologically, the former each consists of numerous scattered tiny tumors while the latter each has one or a few solid biomasses. The former tends to be more aggressive and takes place in younger patients than the latter.

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Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs.

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Transcriptional activation of p21 (cyclin-dependent kinase inhibitor 1A) due to DNA damage often alters the distribution of histone variant H2A.Z at the p21 gene. However, whether the human INO80 complex regulates changes in H2A.

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The human males absent on the first (MOF)-containing histone acetyltransferase nonspecific lethal (NSL) complex comprises nine subunits including the -linked -acetylglucosamine (-GlcNAc) transferase, isoform 1 (OGT1). However, whether the -GlcNAc transferase activity of OGT1 controls histone acetyltransferase activity of the NSL complex and whether OGT1 physically interacts with the other NSL complex subunits remain unclear. Here, we demonstrate that OGT1 regulates the activity of the NSL complex by mainly acetylating histone H4 Lys-16, Lys-5, and Lys-8 via -GlcNAcylation and stabilization of the NSL complex subunit NSL3.

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