mouse brain dataset (Spatial Transcriptomics Inc)
86
Structured Review
Spatial Transcriptomics Inc
mouse brain dataset
Mouse Brain Dataset, supplied by Spatial Transcriptomics Inc, 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/clock+mouse+brain+spatial+transcriptomics+machine+learning+models/brain+clock+learning+machine+models+mouse+spatial+transcriptomics/pmc12569601-247-5-3
Average 86 stars, based on 1 article reviews
Mouse Brain Dataset, supplied by Spatial Transcriptomics Inc, 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/clock+mouse+brain+spatial+transcriptomics+machine+learning+models/brain+clock+learning+machine+models+mouse+spatial+transcriptomics/pmc12569601-247-5-3
Average 86 stars, based on 1 article reviews
mouse brain dataset - by Bioz Stars,
2026-10
86/100 stars
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Related Articles
Single-cell Transcriptomics:Article Title: Single-cell aging clocks: A precision tool for dissecting and targeting the aging process. Article Snippet: Biological age, an indicator of an individual’s health status, was initially measured using bulk tissue aging clocks.. However, by averaging molecular signals across thousands of cells, these tools mask the cellular heterogeneity that characterizes aging.. Recent single-cell aging clocks, enabled by high-resolution omics technologies, address this limitation. Expressing:Article Title: Single-cell aging clocks: A precision tool for dissecting and targeting the aging process. Article Snippet: Biological age, an indicator of an individual’s health status, was initially measured using bulk tissue aging clocks.. However, by averaging molecular signals across thousands of cells, these tools mask the cellular heterogeneity that characterizes aging.. Recent single-cell aging clocks, enabled by high-resolution omics technologies, address this limitation. Spatial Transcriptomics:Article Title: GTestimate: improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator. Article Snippet: .. UMIs/spot in the Article Title: Distance-Preserving Representations for Genomic Spatial Reconstruction Article Snippet: The spatial context of single-cell gene expression data is crucial for many downstream analyses, yet often remains inaccessible due to practical and technical limitations, restricting the utility of such datasets.. In this paper, we propose a generic representation learning and transfer learning framework dp-VAE, capable of reconstructing the spatial coordinates associated with the provided gene expression data.. Central to our approach is a distance-preserving regularizer integrated into the loss function during training, ensuring the model effectively captures and utilizes spatial context signals from reference datasets. other:Article Title: GTestimate: improving relative gene expression estimation in scRNA-seq using the Good–Turing estimator Article Snippet: UMIs/spot in the |