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multi omics data integration approaches  (Omics Data Automation)

 
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    Omics Data Automation multi omics data integration approaches
    Multi Omics Data Integration Approaches, supplied by Omics Data Automation, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/data+integration/approach+based+data+deep+integration+learning+multi+omics/pm42043678-521-4-26
    Average 86 stars, based on 1 article reviews
    multi omics data integration approaches - by Bioz Stars, 2026-10
    86/100 stars

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    other:

    Article Title: Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.
    Article Snippet: [85] S. Yang, Z. Wang, C. Wang, C. Li, and B. Wang, “Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach,” J.

    Biomarker Discovery:

    Article Title: The crosstalk between epigenetics and metabolism in the malignant cell.
    Article Snippet: .. Different ways that these multi-omics data integration approaches utilize these different types of omics datasets include early integration (combining raw data sets), intermediate integration (combining processed omics data features), and late integration (combining multiple analysis results). ..



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    Single-cell analysis of HCC TME and hepatocyte metabolic reprogramming. (A) UMAP dimensionality reduction plot showing 22 initial cell clusters. (B) Annotation of 7 major cell populations based on established marker genes. (C) Expression patterns of CD274 (PD-L1) and CTLA4 in various cell types before and after immunotherapy. (D) Cellular composition across samples. Sample H68 exhibits the highest proportion of Hepatocytes. (E) UMAP plot of hepatocyte subclustering analysis based on metabolic-related gene expression profiles, identifying 5 subpopulations (clusters 0-4). Cluster 1 is significantly enriched in post-treatment samples. (F) KEGG pathway enrichment analysis reveals functional clustering of hepatocyte subpopulations (clusters 0-4). (G) Hierarchical clustering of significantly enriched pathways within these clusters. (H) Expression patterns of core genes within the Pyruvate metabolism pathway across the five hepatocyte subpopulations. Genes in this pathway exhibit significantly elevated expression in Cluster 1.

    Journal: Frontiers in Immunology

    Article Title: The inhibitory effect of hepatic cancer energy metabolism on immune checkpoint therapy: perspectives from single-cell multi-omics analysis

    doi: 10.3389/fimmu.2026.1753670

    Figure Lengend Snippet: Single-cell analysis of HCC TME and hepatocyte metabolic reprogramming. (A) UMAP dimensionality reduction plot showing 22 initial cell clusters. (B) Annotation of 7 major cell populations based on established marker genes. (C) Expression patterns of CD274 (PD-L1) and CTLA4 in various cell types before and after immunotherapy. (D) Cellular composition across samples. Sample H68 exhibits the highest proportion of Hepatocytes. (E) UMAP plot of hepatocyte subclustering analysis based on metabolic-related gene expression profiles, identifying 5 subpopulations (clusters 0-4). Cluster 1 is significantly enriched in post-treatment samples. (F) KEGG pathway enrichment analysis reveals functional clustering of hepatocyte subpopulations (clusters 0-4). (G) Hierarchical clustering of significantly enriched pathways within these clusters. (H) Expression patterns of core genes within the Pyruvate metabolism pathway across the five hepatocyte subpopulations. Genes in this pathway exhibit significantly elevated expression in Cluster 1.

    Article Snippet: Integrated single-cell RNA-seq data (GEO, Mendeley) were analyzed using Seurat, AUCell, pySCENIC, CellChat, and Monocle.

    Techniques: Single-cell Analysis, Marker, Expressing, Gene Expression, Functional Assay