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Epigenomics ag spatial multi-omics
Spatial Multi Omics, supplied by Epigenomics ag, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Article Title: Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images.
Article Snippet: Although some previous studies examined the prediction of molecular targets, the current study found that prognostic histopathological patterns for bladder cancer are rather associated with multi-omics profiles (i.e., transcriptomic, genomics, and epigenomics); these multi-omics profiles are already covering the specific molecular subtypes and the FGFR3 mutations investigated earlier, and we have shown that the accuracy of our risk groups for FGFR3 mutation is similar to the previous report, signifying the impact of multi-omics profiles as confounding factors on the results of earlier studies.

Article Title: Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.
Article Snippet: The distributions of AV45 and FDG are shown in 0.0 0.2 0.4 0.6 Transcriptomics Genomics Epigenomics Multi−omics A 0 10 20 30 Transcriptomics Genomics Epigenomics Multi−omics −0.2 0.0 0.2 0.4 0.6 Transcriptomics Genomics Epigenomics Multi−omics B 0 10 20 30 40 Transcriptomics Genomics Epigenomics Multi−omics −0.2 0.0 0.2 0.4 0.6 Transcriptomics Genomics Epigenomics Multi−omics C 0 10 20 30 Transcriptomics Genomics Epigenomics Multi−omics −0.25 0.00 0.25 0.50 Transcriptomics Genomics Epigenomics Multi−omics D 0 100 200 300 400 Transcriptomics Genomics Epigenomics Multi−omics 0.00 0.25 0.50 0.75 Transcriptomics Genomics Epigenomics Multi−omics E 0 5 10 15 Transcriptomics Genomics Epigenomics Multi−omics TBLMM−LIN TBLMM OmicKriging Pearson Correlation Root Mean Square Errors Figure 1.

Article Title: Advancements in Biologic Therapies for Pediatric Asthma: Emerging Therapies and Future Directions
Article Snippet: Role of Multi-omics Approaches Multi-omics approaches, which integrate data from genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics (environmental exposures), are providing new insights into the complexity of childhood asthma and potential therapeutic targets.

Article Title: Proceedings of the 31st European Paediatric Rheumatology Congress: part 1.
Article Snippet: Methods: We have undertaken a project aimed at analyzing available data and producing new data from a total of 200 SAIDs and uSAIDs, by integrating structured clinical data, multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics, lipidomics, epigenomics, immunomics and inflammatory panel) and Artificial Intelligence technologies.

Article Title: Spatial omics enters the microscopic realm: opportunities and challenges.
Article Snippet: Persistent challenges in cell segmentation and cross-platform integration require the development of segmentation-free approaches and Spatial transcriptomics (ST) enables systematic profiling of whole-transcriptome gene expression in tissues while preserving spatial context.. Recent advances in sequencingand imaging-based ST technologies have ushered in the era of microscopic-resolution ST (μST), allowing transcriptome mapping at cellular and even subcellular scales with unprecedented precision.. Despite these advances, μST faces substantial challenges, including sparse transcript discovery per submicron (or micron)-sized spatial units and data fragmentation across platforms, hindering integration and analysis.

Article Title: Prenatal pesticide exposure and adverse reproductive outcomes: Epidemiological evidence and mechanistic insights.
Article Snippet: • First comprehensive review on pesticide exposure and maternal and neonatal

Transcriptomics:

Article Title: Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics
Article Snippet: .. Spatial multiomics technologies (including genomics, epigenomics, transcriptomics, proteomics, and metabolomics) can integrate biological molecules and their physical spatial locations, which may systematically advance tumor research (Fig. A). ..

Biomarker Discovery:

Article Title: A cloud-based learning module for biomarker discovery
Article Snippet: .. As of this writing, existing modules that complement the biomarker discovery module include RNA-seq, proteomics, single-cell ATAC-seq and multiomics (transcriptomics + epigenomics). ..

RNA Sequencing:

Article Title: A cloud-based learning module for biomarker discovery
Article Snippet: .. As of this writing, existing modules that complement the biomarker discovery module include RNA-seq, proteomics, single-cell ATAC-seq and multiomics (transcriptomics + epigenomics). ..



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a SEPAR is a framework based on graph-regularized NMF designed to identify spatially aware metagene patterns using both spatial location and gene expression as input. Spatial location data is leveraged to construct weighted graph regularization capturing the spatial relationships of the spots or cells. Sparsity regularization and dissimilarity regularization are applied to ensure distinct spatial metagene patterns. b SEPAR supports efficient and robust downstream analyses of SRT data, including metagene expression pattern recognition, pattern-specific gene analysis, SVG identification, spatial domain delineation, gene expression denoising and <t>spatial</t> <t>multi-omics</t> data analysis.
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Technologies for assaying intratumoral heterogeneity.
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a SEPAR is a framework based on graph-regularized NMF designed to identify spatially aware metagene patterns using both spatial location and gene expression as input. Spatial location data is leveraged to construct weighted graph regularization capturing the spatial relationships of the spots or cells. Sparsity regularization and dissimilarity regularization are applied to ensure distinct spatial metagene patterns. b SEPAR supports efficient and robust downstream analyses of SRT data, including metagene expression pattern recognition, pattern-specific gene analysis, SVG identification, spatial domain delineation, gene expression denoising and spatial multi-omics data analysis.

Journal: Communications Biology

Article Title: SEPAR enables spatial metagene discovery and associated molecular pattern characterization in spatial transcriptomics and multi-omics datasets

doi: 10.1038/s42003-025-09340-w

Figure Lengend Snippet: a SEPAR is a framework based on graph-regularized NMF designed to identify spatially aware metagene patterns using both spatial location and gene expression as input. Spatial location data is leveraged to construct weighted graph regularization capturing the spatial relationships of the spots or cells. Sparsity regularization and dissimilarity regularization are applied to ensure distinct spatial metagene patterns. b SEPAR supports efficient and robust downstream analyses of SRT data, including metagene expression pattern recognition, pattern-specific gene analysis, SVG identification, spatial domain delineation, gene expression denoising and spatial multi-omics data analysis.

Article Snippet: Beyond demonstrating robust performance in diverse spatial transcriptomics technologies (10 × Visium, Stereo-seq, osmFISH and MERFISH), where SEPAR consistently identified biologically meaningful spatial patterns and revealed tissue-specific expression programs across different resolution scales and measurement principles, SEPAR effectively handles multi-omics spatial molecular data.

Techniques: Gene Expression, Construct, Expressing, Biomarker Discovery

Technologies for assaying intratumoral heterogeneity.

Journal: Cancers

Article Title: Heterogeneity in Cancer

doi: 10.3390/cancers17030441

Figure Lengend Snippet: Technologies for assaying intratumoral heterogeneity.

Article Snippet: Spatial multi-omics (e.g., GeoMX) , Combines IF with spatial transcriptomics through UV cleavage of ROIs , High-throughput spatial profiling of both transcriptome and protein panel in a tissue sample , [ ] .

Techniques: Gene Expression, Fluorescence, Expressing, Protein-Protein interactions, Activity Assay, In Situ, Biomarker Discovery