kohonen self-organizing map (som) Search Results


chemical fingerprints version 6.2.1  (ChemAxon LLC)
90
ChemAxon LLC chemical fingerprints version 6.2.1
Chemical Fingerprints Version 6.2.1, supplied by ChemAxon LLC, 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/kohonen+self-organizing+map+(som)/chemical+fingerprints/10__1289_slash_ehp2589-112-21-17
Average 90 stars, based on 1 article reviews
chemical fingerprints version 6.2.1 - by Bioz Stars, 2026-08
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self organized kohonen maps  (Siemens AG)
90
Siemens AG self organized kohonen maps
Self Organized Kohonen Maps, supplied by Siemens AG, 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/kohonen+self-organizing+map+(som)/self+organized+kohonen+maps/10__1007_slash_bf03405813-677-8-22
Average 90 stars, based on 1 article reviews
self organized kohonen maps - by Bioz Stars, 2026-08
90/100 stars
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som (self-organizing map)  (Verlag GmbH)
90
Verlag GmbH som (self-organizing map)
Som (Self Organizing Map), supplied by Verlag GmbH, 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/kohonen+self-organizing+map+(som)/self+organising+maps/us07133860-31-9-19
Average 90 stars, based on 1 article reviews
som (self-organizing map) - by Bioz Stars, 2026-08
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self-organising maps (soms, also referred to as kohonen maps) viscovery profiler v.7.1  (Viscovery Software GmbH)
90
Viscovery Software GmbH self-organising maps (soms, also referred to as kohonen maps) viscovery profiler v.7.1
Self Organising Maps (Soms, Also Referred To As Kohonen Maps) Viscovery Profiler V.7.1, supplied by Viscovery Software GmbH, 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/kohonen+self-organizing+map+(som)/self+organising+maps++soms++also+referred+to+as+kohonen+maps++viscovery+profiler+v+7+1/pmc09990620-154-1-28
Average 90 stars, based on 1 article reviews
self-organising maps (soms, also referred to as kohonen maps) viscovery profiler v.7.1 - by Bioz Stars, 2026-08
90/100 stars
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kohonen artificial neural networks  (Verlag GmbH)
90
Verlag GmbH kohonen artificial neural networks
Kohonen Artificial Neural Networks, supplied by Verlag GmbH, 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/kohonen+self-organizing+map+(som)/kohonen+artificial+neural+networks/us07653491-305-5-15
Average 90 stars, based on 1 article reviews
kohonen artificial neural networks - by Bioz Stars, 2026-08
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engene software  (enGene Inc)
90
enGene Inc engene software
Engene Software, supplied by enGene 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/kohonen+self-organizing+map+(som)/engene+software+package/us08871219-520-38-51
Average 90 stars, based on 1 article reviews
engene software - by Bioz Stars, 2026-08
90/100 stars
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visual explorations in finance with self-organizing maps  (Verlag GmbH)
90
Verlag GmbH visual explorations in finance with self-organizing maps
Visual Explorations In Finance With Self Organizing Maps, supplied by Verlag GmbH, 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/kohonen+self-organizing+map+(som)/visual+explorations+in+finance+with+self+organizing+maps/10__1057_slash_palgrave__jt__5740056-199-5-14
Average 90 stars, based on 1 article reviews
visual explorations in finance with self-organizing maps - by Bioz Stars, 2026-08
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rstudio 1.0.136  (RStudio)
90
RStudio rstudio 1.0.136
Rstudio 1.0.136, supplied by RStudio, 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/kohonen+self-organizing+map+(som)/rstudio+version+1+3+1093/10__1038_slash_s41589___018___0078___4-233-7-8
Average 90 stars, based on 1 article reviews
rstudio 1.0.136 - by Bioz Stars, 2026-08
90/100 stars
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proteome quest  (Correlogic Systems Inc)
90
Correlogic Systems Inc proteome quest
Proteome Quest, supplied by Correlogic Systems 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/kohonen+self-organizing+map+(som)/proteomequest/us08478534-40-28-0
Average 90 stars, based on 1 article reviews
proteome quest - by Bioz Stars, 2026-08
90/100 stars
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unsupervised self-organizing map (som) algorithm  (RStudio)
90
RStudio unsupervised self-organizing map (som) algorithm
Exploratory data analysis. ( a ) <t>(i)</t> <t>PCA</t> plot for PC1 vs. PC3 (all spectra); ( a ) (ii) Eigenvalue plot for PC3. ( b ) (i) Scree plot for variance explained for PCs 1–4. Blue line shows inflection point between PC2 and PC3, or PC3 and PC4 (scree test) indicating either two or three PCs should be retained (dotted lines). ( b ) (ii) Log eigenvalue plot indicating that the first three PCs should be retained (eigenvalue > 1 test). ( c ) (i) Self organised map <t>(SOM)</t> codes plot with four categories identified by hierarchical cluster analysis (HCA): green circles (two-peak), yellow circles (major shoulder), red circles (single peak), blue circles (minor shoulder). ( c ) (ii) SOM heatmap with inset circles showing intra-nodal Euclidean distance. ( c ) (iii) Mountain plot showing inter-nodal Euclidean distance. ( d ) HCA dendrogram indicating clusters in ( c ) (i). * represents one anomalous spectrum that has been classified into its own node in the self-organizing map.
Unsupervised Self Organizing Map (Som) Algorithm, supplied by RStudio, 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/kohonen+self-organizing+map+(som)/unsupervised+self+organizing+map++som++algorithm/pmc10255221-145-9-17
Average 90 stars, based on 1 article reviews
unsupervised self-organizing map (som) algorithm - by Bioz Stars, 2026-08
90/100 stars
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principal component analysis (pca)  (RStudio)
90
RStudio principal component analysis (pca)
Exploratory data analysis. ( a ) (i) <t>PCA</t> plot for PC1 vs. PC3 (all spectra); ( a ) (ii) Eigenvalue plot for PC3. ( b ) (i) Scree plot for variance explained for PCs 1–4. Blue line shows inflection point between PC2 and PC3, or PC3 and PC4 (scree test) indicating either two or three PCs should be retained (dotted lines). ( b ) (ii) Log eigenvalue plot indicating that the first three PCs should be retained (eigenvalue > 1 test). ( c ) (i) Self organised <t>map</t> <t>(SOM)</t> codes plot with four categories identified by hierarchical cluster analysis (HCA): green circles (two-peak), yellow circles (major shoulder), red circles (single peak), blue circles (minor shoulder). ( c ) (ii) SOM heatmap with inset circles showing intra-nodal Euclidean distance. ( c ) (iii) Mountain plot showing inter-nodal Euclidean distance. ( d ) HCA dendrogram indicating clusters in ( c ) (i). * represents one anomalous spectrum that has been classified into its own node in the self-organizing map.
Principal Component Analysis (Pca), supplied by RStudio, 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/kohonen+self-organizing+map+(som)/principal+component+analysis++pca+/pmc10255221-145-1-17
Average 90 stars, based on 1 article reviews
principal component analysis (pca) - by Bioz Stars, 2026-08
90/100 stars
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genecluster 2.1.7 (som)  (Broad Institute Inc)
90
Broad Institute Inc genecluster 2.1.7 (som)
Exploratory data analysis. ( a ) (i) <t>PCA</t> plot for PC1 vs. PC3 (all spectra); ( a ) (ii) Eigenvalue plot for PC3. ( b ) (i) Scree plot for variance explained for PCs 1–4. Blue line shows inflection point between PC2 and PC3, or PC3 and PC4 (scree test) indicating either two or three PCs should be retained (dotted lines). ( b ) (ii) Log eigenvalue plot indicating that the first three PCs should be retained (eigenvalue > 1 test). ( c ) (i) Self organised <t>map</t> <t>(SOM)</t> codes plot with four categories identified by hierarchical cluster analysis (HCA): green circles (two-peak), yellow circles (major shoulder), red circles (single peak), blue circles (minor shoulder). ( c ) (ii) SOM heatmap with inset circles showing intra-nodal Euclidean distance. ( c ) (iii) Mountain plot showing inter-nodal Euclidean distance. ( d ) HCA dendrogram indicating clusters in ( c ) (i). * represents one anomalous spectrum that has been classified into its own node in the self-organizing map.
Genecluster 2.1.7 (Som), supplied by Broad Institute 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/kohonen+self-organizing+map+(som)/genecluster2/pmc02664812-135-13-19
Average 90 stars, based on 1 article reviews
genecluster 2.1.7 (som) - by Bioz Stars, 2026-08
90/100 stars
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Image Search Results


Exploratory data analysis. ( a ) (i) PCA plot for PC1 vs. PC3 (all spectra); ( a ) (ii) Eigenvalue plot for PC3. ( b ) (i) Scree plot for variance explained for PCs 1–4. Blue line shows inflection point between PC2 and PC3, or PC3 and PC4 (scree test) indicating either two or three PCs should be retained (dotted lines). ( b ) (ii) Log eigenvalue plot indicating that the first three PCs should be retained (eigenvalue > 1 test). ( c ) (i) Self organised map (SOM) codes plot with four categories identified by hierarchical cluster analysis (HCA): green circles (two-peak), yellow circles (major shoulder), red circles (single peak), blue circles (minor shoulder). ( c ) (ii) SOM heatmap with inset circles showing intra-nodal Euclidean distance. ( c ) (iii) Mountain plot showing inter-nodal Euclidean distance. ( d ) HCA dendrogram indicating clusters in ( c ) (i). * represents one anomalous spectrum that has been classified into its own node in the self-organizing map.

Journal: Sensors (Basel, Switzerland)

Article Title: Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence

doi: 10.3390/s23115149

Figure Lengend Snippet: Exploratory data analysis. ( a ) (i) PCA plot for PC1 vs. PC3 (all spectra); ( a ) (ii) Eigenvalue plot for PC3. ( b ) (i) Scree plot for variance explained for PCs 1–4. Blue line shows inflection point between PC2 and PC3, or PC3 and PC4 (scree test) indicating either two or three PCs should be retained (dotted lines). ( b ) (ii) Log eigenvalue plot indicating that the first three PCs should be retained (eigenvalue > 1 test). ( c ) (i) Self organised map (SOM) codes plot with four categories identified by hierarchical cluster analysis (HCA): green circles (two-peak), yellow circles (major shoulder), red circles (single peak), blue circles (minor shoulder). ( c ) (ii) SOM heatmap with inset circles showing intra-nodal Euclidean distance. ( c ) (iii) Mountain plot showing inter-nodal Euclidean distance. ( d ) HCA dendrogram indicating clusters in ( c ) (i). * represents one anomalous spectrum that has been classified into its own node in the self-organizing map.

Article Snippet: Modelling: Principal component analysis (PCA) and an unsupervised self-organizing map (SOM) algorithm were conducted for data exploration (R Studio, Kohonen package) [ ].

Techniques:

Exploratory data analysis. ( a ) (i) PCA plot for PC1 vs. PC3 (all spectra); ( a ) (ii) Eigenvalue plot for PC3. ( b ) (i) Scree plot for variance explained for PCs 1–4. Blue line shows inflection point between PC2 and PC3, or PC3 and PC4 (scree test) indicating either two or three PCs should be retained (dotted lines). ( b ) (ii) Log eigenvalue plot indicating that the first three PCs should be retained (eigenvalue > 1 test). ( c ) (i) Self organised map (SOM) codes plot with four categories identified by hierarchical cluster analysis (HCA): green circles (two-peak), yellow circles (major shoulder), red circles (single peak), blue circles (minor shoulder). ( c ) (ii) SOM heatmap with inset circles showing intra-nodal Euclidean distance. ( c ) (iii) Mountain plot showing inter-nodal Euclidean distance. ( d ) HCA dendrogram indicating clusters in ( c ) (i). * represents one anomalous spectrum that has been classified into its own node in the self-organizing map.

Journal: Sensors (Basel, Switzerland)

Article Title: Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence

doi: 10.3390/s23115149

Figure Lengend Snippet: Exploratory data analysis. ( a ) (i) PCA plot for PC1 vs. PC3 (all spectra); ( a ) (ii) Eigenvalue plot for PC3. ( b ) (i) Scree plot for variance explained for PCs 1–4. Blue line shows inflection point between PC2 and PC3, or PC3 and PC4 (scree test) indicating either two or three PCs should be retained (dotted lines). ( b ) (ii) Log eigenvalue plot indicating that the first three PCs should be retained (eigenvalue > 1 test). ( c ) (i) Self organised map (SOM) codes plot with four categories identified by hierarchical cluster analysis (HCA): green circles (two-peak), yellow circles (major shoulder), red circles (single peak), blue circles (minor shoulder). ( c ) (ii) SOM heatmap with inset circles showing intra-nodal Euclidean distance. ( c ) (iii) Mountain plot showing inter-nodal Euclidean distance. ( d ) HCA dendrogram indicating clusters in ( c ) (i). * represents one anomalous spectrum that has been classified into its own node in the self-organizing map.

Article Snippet: Modelling: Principal component analysis (PCA) and an unsupervised self-organizing map (SOM) algorithm were conducted for data exploration (R Studio, Kohonen package) [ ].

Techniques: