matrix decomposition Search Results


matrix decomposition  (EcoDesign Inc)
90
EcoDesign Inc matrix decomposition
Matrix Decomposition, supplied by EcoDesign Inc, 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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matrix decomposition - by Bioz Stars, 2026-08
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cholesky decomposition matrix  (Barents Group LLC)
90
Barents Group LLC cholesky decomposition matrix
Cholesky Decomposition Matrix, supplied by Barents Group LLC, 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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cholesky decomposition matrix - by Bioz Stars, 2026-08
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eigenvector decomposition of the covariance or correlation matrix  (Marhaba Laboratories)
90
Marhaba Laboratories eigenvector decomposition of the covariance or correlation matrix
Eigenvector Decomposition Of The Covariance Or Correlation Matrix, supplied by Marhaba Laboratories, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/matrix+decomposition/eigenvector+decomposition+of+the+covariance+or+correlation+matrix/pm30128973-814-8-14
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eigenvector decomposition of the covariance or correlation matrix - by Bioz Stars, 2026-08
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matrix sign function decomposition with newton’s iterative method  (Balzer GmbH)
90
Balzer GmbH matrix sign function decomposition with newton’s iterative method
Matrix Sign Function Decomposition With Newton’s Iterative Method, supplied by Balzer GmbH, 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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matrix sign function decomposition with newton’s iterative method - by Bioz Stars, 2026-08
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spectral matrix decomposition-based motion artifacts removal in multi-channel ppg sensor signals  (IEEE Access)
90
IEEE Access spectral matrix decomposition-based motion artifacts removal in multi-channel ppg sensor signals
Spectral Matrix Decomposition Based Motion Artifacts Removal In Multi Channel Ppg Sensor Signals, supplied by IEEE Access, 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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spectral matrix decomposition-based motion artifacts removal in multi-channel ppg sensor signals - by Bioz Stars, 2026-08
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matrix decomposition  (Spatial Transcriptomics Inc)
86
Spatial Transcriptomics Inc matrix decomposition
Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
Matrix Decomposition, 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
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matrix decomposition - by Bioz Stars, 2026-08
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF decomposition strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.

Journal: Advanced Science

Article Title: Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES

doi: 10.1002/advs.202508346

Figure Lengend Snippet: Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF decomposition strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.

Article Snippet: Wu , and D. Sun , “ Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES .” Adv.

Techniques: Construct, Gene Expression