som neural network toolbox Search Results


som neural network toolbox  (MathWorks Inc)
90
MathWorks Inc som neural network toolbox
Unrooted dendrogram representing relationships among Fargesia and Thamnocalamus species estimated with a <t>SOM</t> neural network. For all characters, the mean value was used to construct the dendrogram. Tree distances were calculated with the SOM Neural Network Toolbox <t>for</t> <t>MATLAB</t> software (MathWorks Inc., Natick, MA, USA) and the online tool Interactive Tree Of Life (ITOL; http://itol.embl.de/ ).
Som Neural Network Toolbox, 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
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neural network toolbox  (MathWorks Inc)
90
MathWorks Inc neural network toolbox
Evaluation and quantification of plant water status during desiccation and recovery of Haberlea rhodopen sis. (A) Drying and recovery of different plants (upper left, Control plants; upper right 120 h of desiccation; lower left, 192 h of desiccation; lower right 36 h of recovery). (B) Box plots of changes in water content during desiccation (blue) and recovery (green). (C) Self organizing map <t>(SOM)</t> visualization of the classified samples according to the four pre-defined neurons for desiccation and recovery. The positions of the scores (leaf samples) derived after Principal Component <t>Analysis</t> <t>(PCA)</t> transformation are clustered in different colors with the neurons according to their weights defined with the two weight vectors, for desiccation (left) and for recovery (right), respectively.
Neural Network Toolbox, 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/result/neural network toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
neural network toolbox - by Bioz Stars, 2026-03
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self organizing maps (som; from the neural network toolbox, matlab r2009b)  (MathWorks Inc)
90
MathWorks Inc self organizing maps (som; from the neural network toolbox, matlab r2009b)
Evaluation and quantification of plant water status during desiccation and recovery of Haberlea rhodopen sis. (A) Drying and recovery of different plants (upper left, Control plants; upper right 120 h of desiccation; lower left, 192 h of desiccation; lower right 36 h of recovery). (B) Box plots of changes in water content during desiccation (blue) and recovery (green). (C) Self organizing map <t>(SOM)</t> visualization of the classified samples according to the four pre-defined neurons for desiccation and recovery. The positions of the scores (leaf samples) derived after Principal Component <t>Analysis</t> <t>(PCA)</t> transformation are clustered in different colors with the neurons according to their weights defined with the two weight vectors, for desiccation (left) and for recovery (right), respectively.
Self Organizing Maps (Som; From The Neural Network Toolbox, Matlab R2009b), 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/result/self organizing maps (som; from the neural network toolbox, matlab r2009b)/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
self organizing maps (som; from the neural network toolbox, matlab r2009b) - by Bioz Stars, 2026-03
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som implementation neural network toolbox  (MathWorks Inc)
90
MathWorks Inc som implementation neural network toolbox
Evaluation and quantification of plant water status during desiccation and recovery of Haberlea rhodopen sis. (A) Drying and recovery of different plants (upper left, Control plants; upper right 120 h of desiccation; lower left, 192 h of desiccation; lower right 36 h of recovery). (B) Box plots of changes in water content during desiccation (blue) and recovery (green). (C) Self organizing map <t>(SOM)</t> visualization of the classified samples according to the four pre-defined neurons for desiccation and recovery. The positions of the scores (leaf samples) derived after Principal Component <t>Analysis</t> <t>(PCA)</t> transformation are clustered in different colors with the neurons according to their weights defined with the two weight vectors, for desiccation (left) and for recovery (right), respectively.
Som Implementation Neural Network Toolbox, 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/result/som implementation neural network toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
som implementation neural network toolbox - by Bioz Stars, 2026-03
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neural network toolbox 10.0  (MathWorks Inc)
90
MathWorks Inc neural network toolbox 10.0
Evaluation and quantification of plant water status during desiccation and recovery of Haberlea rhodopen sis. (A) Drying and recovery of different plants (upper left, Control plants; upper right 120 h of desiccation; lower left, 192 h of desiccation; lower right 36 h of recovery). (B) Box plots of changes in water content during desiccation (blue) and recovery (green). (C) Self organizing map <t>(SOM)</t> visualization of the classified samples according to the four pre-defined neurons for desiccation and recovery. The positions of the scores (leaf samples) derived after Principal Component <t>Analysis</t> <t>(PCA)</t> transformation are clustered in different colors with the neurons according to their weights defined with the two weight vectors, for desiccation (left) and for recovery (right), respectively.
Neural Network Toolbox 10.0, 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/result/neural network toolbox 10.0/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
neural network toolbox 10.0 - by Bioz Stars, 2026-03
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Image Search Results


Unrooted dendrogram representing relationships among Fargesia and Thamnocalamus species estimated with a SOM neural network. For all characters, the mean value was used to construct the dendrogram. Tree distances were calculated with the SOM Neural Network Toolbox for MATLAB software (MathWorks Inc., Natick, MA, USA) and the online tool Interactive Tree Of Life (ITOL; http://itol.embl.de/ ).

Journal: Scientific Reports

Article Title: Differentiating Thamnocalamus Munro from Fargesia Franchet emend . Yi (Bambusoideae, Poaceae): novel evidence from morphological and neural-network analyses

doi: 10.1038/s41598-017-04613-9

Figure Lengend Snippet: Unrooted dendrogram representing relationships among Fargesia and Thamnocalamus species estimated with a SOM neural network. For all characters, the mean value was used to construct the dendrogram. Tree distances were calculated with the SOM Neural Network Toolbox for MATLAB software (MathWorks Inc., Natick, MA, USA) and the online tool Interactive Tree Of Life (ITOL; http://itol.embl.de/ ).

Article Snippet: Tree distances were calculated with the SOM Neural Network Toolbox for MATLAB software (MathWorks Inc., Natick, MA, USA) and the online tool Interactive Tree Of Life (ITOL; http://itol.embl.de/ ).

Techniques: Construct, Software

Trained classification structure model ( A ) and weight structure ( B , C and D ) of the SOM neural network. We converted the 46 morphological characters into normalized vectors of codon usage x( t ), and 32 accessions were classified by character factors. Symmetrical effects and differences among the samples are more obvious and significant, although off-diagonal weight points are observed.

Journal: Scientific Reports

Article Title: Differentiating Thamnocalamus Munro from Fargesia Franchet emend . Yi (Bambusoideae, Poaceae): novel evidence from morphological and neural-network analyses

doi: 10.1038/s41598-017-04613-9

Figure Lengend Snippet: Trained classification structure model ( A ) and weight structure ( B , C and D ) of the SOM neural network. We converted the 46 morphological characters into normalized vectors of codon usage x( t ), and 32 accessions were classified by character factors. Symmetrical effects and differences among the samples are more obvious and significant, although off-diagonal weight points are observed.

Article Snippet: Tree distances were calculated with the SOM Neural Network Toolbox for MATLAB software (MathWorks Inc., Natick, MA, USA) and the online tool Interactive Tree Of Life (ITOL; http://itol.embl.de/ ).

Techniques:

Evaluation and quantification of plant water status during desiccation and recovery of Haberlea rhodopen sis. (A) Drying and recovery of different plants (upper left, Control plants; upper right 120 h of desiccation; lower left, 192 h of desiccation; lower right 36 h of recovery). (B) Box plots of changes in water content during desiccation (blue) and recovery (green). (C) Self organizing map (SOM) visualization of the classified samples according to the four pre-defined neurons for desiccation and recovery. The positions of the scores (leaf samples) derived after Principal Component Analysis (PCA) transformation are clustered in different colors with the neurons according to their weights defined with the two weight vectors, for desiccation (left) and for recovery (right), respectively.

Journal: Frontiers in Plant Science

Article Title: In vivo spectroscopy and NMR metabolite fingerprinting approaches to connect the dynamics of photosynthetic and metabolic phenotypes in resurrection plant Haberlea rhodopensis during desiccation and recovery

doi: 10.3389/fpls.2015.00564

Figure Lengend Snippet: Evaluation and quantification of plant water status during desiccation and recovery of Haberlea rhodopen sis. (A) Drying and recovery of different plants (upper left, Control plants; upper right 120 h of desiccation; lower left, 192 h of desiccation; lower right 36 h of recovery). (B) Box plots of changes in water content during desiccation (blue) and recovery (green). (C) Self organizing map (SOM) visualization of the classified samples according to the four pre-defined neurons for desiccation and recovery. The positions of the scores (leaf samples) derived after Principal Component Analysis (PCA) transformation are clustered in different colors with the neurons according to their weights defined with the two weight vectors, for desiccation (left) and for recovery (right), respectively.

Article Snippet: To classify the stress states according to the similarity of JIP parameters, we clustered the scores from the first tree components of PCA under a typical self organizing map (SOM) scheme (neural network toolbox MatLab).

Techniques: Control, Derivative Assay, Transformation Assay