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non-negative matrix factorization matlab nnmf function  (MathWorks Inc)


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    MathWorks Inc non-negative matrix factorization matlab nnmf function
    Non Negative Matrix Factorization Matlab Nnmf Function, supplied by MathWorks Inc, 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/non-negative+matrix+factorization+(nnmf)+function/pm40140582-769-20-23
    Average 90 stars, based on 1 article reviews
    non-negative matrix factorization matlab nnmf function - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Atypical cortical networks in children at high-genetic risk of psychiatric and neurodevelopmental disorders.
    Article Snippet: In order to reduce the dimensionality of these features before statistical analyses, we used a data-driven analysis of the principal components using non-negative matrix factorization (NNMF, Matlab: nnmf).

    Article Title: A left-lateralized dorsolateral prefrontal network for naming.
    Article Snippet: We used non-negative matrix factorization (NMF, nnmf function, MATLAB) as a soft clustering technique to cluster the neural data.

    Article Title: Common multi-day rhythms in smartphone behavior.
    Article Snippet: The non-negative matrix factorization (mexTrainDL, spams package for MATLAB, Inria) yielded two matrices: a meta-rhythms matrix W 2 RT 0 ́ Rand a meta-behavior matrix H 2 RR ́ 2500 where R is the optimal factorization rank.

    Article Title: Oxidation of retromer complex controls mitochondrial translation.
    Article Snippet: Junbing Zhang1,2,14 ✉, Md Yousuf Ali, Harrison Byron Chong, Pei-Chieh Tien, James Woods, Carolina Noble, Tristan Vornbäumen, Zehra Ordulu, Anthony P. Possemato, Stefan Harry, Jay Miguel Fonticella, Lina Fellah, Drew Harrison, Maolin Ge, Neha Khandelwal, Yingfei Huang, Maëva Chauvin, Anica Tamara Bischof, Grace Marie Hambelton, Magdy Farag Gohar, Siwen Zhang, MinGyu Choi, Sara Bouberhan, Esther Oliva, Mari Mino-Kenudson, Natalya N. Pavlova, Michael Lawrence, Justin F. Gainor, Sean A. Beausoleil, Nabeel Bardeesy, Raul Mostoslavsky, David Pépin, Christopher J. Ott, Brian Liau & Liron Bar-Peled1,7,10 ✉

    Article Title: Muscle Recruitment Strategies in a Redundant Task: Age Differences Through Network Analyses.
    Article Snippet: The overall analysis procedure was similar for the three methods: we concatenated the data of interest, filtered it to avoid high frequency oscillations created by concatenation, and performed the non-negative matrix factorization (nnmf code in Matlab).

    Article Title: Neuromuscular mechanisms of motor adaptation to repeated gait-slip perturbations in older adults
    Article Snippet: Muscle synergies were extracted from EMG matrices with unit variance for each condition (novel, early-, and late-adaptation) by non-negative matrix factorization using customized Matlab routines , which is a decomposition algorithm used extensively in muscle synergy analysis , , .

    Article Title: Cellular architecture of human brain metastases.
    Article Snippet: Identification of recurrent expression programs in human brain metastases with Non-Negative Matrix Factorization (NMF) For tumor cells in each sample, Non-Negative Matrix Factorization (Matlab nnmf function, with number of factors set to 10) was applied to the scaled and centered expression data (Seurat ScaleData function), after converting all negative values to zero.

    Article Title: Cellular architecture of human brain metastases.
    Article Snippet: As previously described in human data analysis, for both experimental models, Non-Negative Matrix Factorization (Matlab nnmf function, with number of factors set to 10) was applied to scaled and centered expression data (Seurat ScaleData function), after converting all negative values to zero.



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    MathWorks Inc non-negative matrix factorization matlab nnmf function
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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
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    MathWorks Inc non-negative matrix factorization function nnmf
    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
    Non Negative Matrix Factorization Function Nnmf, supplied by MathWorks Inc, 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/non-negative+matrix+factorization+(nnmf)+function/pmc06898380-502-3-2
    Average 90 stars, based on 1 article reviews
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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Journal: Biomaterials and Biosystems

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy

    doi: 10.1016/j.bbiosy.2022.100059

    Figure Lengend Snippet: Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Article Snippet: Following pre-processing, spectral models were developed using the MATLAB statistics toolbox function non-negative matrix factorization (NNMF) ( c) with in vivo spectra ( a), ex vivo , and reference spectra ( b) as input.

    Techniques: Immunohistochemical staining, Raman Spectroscopy, Derivative Assay, Ex Vivo, In Vivo