built-in functions from matlab’s 3d image processing toolbox Search Results


matlab a pre set parameters  (MathWorks Inc)
93
MathWorks Inc matlab a pre set parameters
Matlab A Pre Set Parameters, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/built-in+functions+from+matlab%E2%80%99s+3d+image+processing+toolbox/pmc09366190__Data_Sheet_1-1-3-3?v=MathWorks+Inc
Average 93 stars, based on 1 article reviews
matlab a pre set parameters - by Bioz Stars, 2026-08
93/100 stars
  Buy from Supplier
stochastic neighbor embedding t sne toolbox  (MathWorks Inc)
96
MathWorks Inc stochastic neighbor embedding t sne toolbox
Data is processed through a traditional pipeline of RNA-seq data preprocessing and differential expression genes (DEGs) extraction using specific filter parameters such as False Discovery Rate (FDR) <0.05 and fold change (FC) >1.5. The data are utilized to construct a correlation matrix, its correlation heatmap is generated to visualize DEGs’ correlation distribution. For further analysis, the absolute values of the correlations are ordered. The sorted heatmap aids in the visualization of the top genes. Clustering is performed using Dendrogram, Principal Component Analysis (PCA), and t-distributed <t>Stochastic</t> <t>Neighbor</t> <t>Embedding</t> (t-SNE) methods, followed by distance thresholding (for the Dendrogram results) or K-means (for the PCA and t-SNE results) for finer clustering. Clusters were analyzed via STRING or network analysis to identify potential target genes. All pathways, including generated and existing ones from databases like GO and KEGG, are quantitatively compared using novel indices and ranked for relevance.
Stochastic Neighbor Embedding T Sne Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/built-in+functions+from+matlab%E2%80%99s+3d+image+processing+toolbox/bio_rxiv__2024__02__11__579390-99-3-41?v=MathWorks+Inc
Average 96 stars, based on 1 article reviews
stochastic neighbor embedding t sne toolbox - by Bioz Stars, 2026-08
96/100 stars
  Buy from Supplier
machine learning toolbox  (MathWorks Inc)
96
MathWorks Inc machine learning toolbox
Data is processed through a traditional pipeline of RNA-seq data preprocessing and differential expression genes (DEGs) extraction using specific filter parameters such as False Discovery Rate (FDR) <0.05 and fold change (FC) >1.5. The data are utilized to construct a correlation matrix, its correlation heatmap is generated to visualize DEGs’ correlation distribution. For further analysis, the absolute values of the correlations are ordered. The sorted heatmap aids in the visualization of the top genes. Clustering is performed using Dendrogram, Principal Component Analysis (PCA), and t-distributed <t>Stochastic</t> <t>Neighbor</t> <t>Embedding</t> (t-SNE) methods, followed by distance thresholding (for the Dendrogram results) or K-means (for the PCA and t-SNE results) for finer clustering. Clusters were analyzed via STRING or network analysis to identify potential target genes. All pathways, including generated and existing ones from databases like GO and KEGG, are quantitatively compared using novel indices and ranked for relevance.
Machine Learning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/built-in+functions+from+matlab%E2%80%99s+3d+image+processing+toolbox/10__1016_slash_j__aca__2018__02__031-118-16-20?v=MathWorks+Inc
Average 96 stars, based on 1 article reviews
machine learning toolbox - by Bioz Stars, 2026-08
96/100 stars
  Buy from Supplier
smartphone iphone 7  (apple inc)
90
apple inc smartphone iphone 7
Data is processed through a traditional pipeline of RNA-seq data preprocessing and differential expression genes (DEGs) extraction using specific filter parameters such as False Discovery Rate (FDR) <0.05 and fold change (FC) >1.5. The data are utilized to construct a correlation matrix, its correlation heatmap is generated to visualize DEGs’ correlation distribution. For further analysis, the absolute values of the correlations are ordered. The sorted heatmap aids in the visualization of the top genes. Clustering is performed using Dendrogram, Principal Component Analysis (PCA), and t-distributed <t>Stochastic</t> <t>Neighbor</t> <t>Embedding</t> (t-SNE) methods, followed by distance thresholding (for the Dendrogram results) or K-means (for the PCA and t-SNE results) for finer clustering. Clusters were analyzed via STRING or network analysis to identify potential target genes. All pathways, including generated and existing ones from databases like GO and KEGG, are quantitatively compared using novel indices and ranked for relevance.
Smartphone Iphone 7, supplied by apple 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/built-in+functions+from+matlab%E2%80%99s+3d+image+processing+toolbox/pmc12262556-91-10-15?v=apple+inc
Average 90 stars, based on 1 article reviews
smartphone iphone 7 - by Bioz Stars, 2026-08
90/100 stars
  Buy from Supplier

Image Search Results


Data is processed through a traditional pipeline of RNA-seq data preprocessing and differential expression genes (DEGs) extraction using specific filter parameters such as False Discovery Rate (FDR) <0.05 and fold change (FC) >1.5. The data are utilized to construct a correlation matrix, its correlation heatmap is generated to visualize DEGs’ correlation distribution. For further analysis, the absolute values of the correlations are ordered. The sorted heatmap aids in the visualization of the top genes. Clustering is performed using Dendrogram, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE) methods, followed by distance thresholding (for the Dendrogram results) or K-means (for the PCA and t-SNE results) for finer clustering. Clusters were analyzed via STRING or network analysis to identify potential target genes. All pathways, including generated and existing ones from databases like GO and KEGG, are quantitatively compared using novel indices and ranked for relevance.

Journal: bioRxiv

Article Title: Identification Drug Targets for Oxaliplatin-Induced Cardiotoxicity without Affecting Cancer Treatment through Inter Variability Cross-Correlation Analysis (IVCCA)

doi: 10.1101/2024.02.11.579390

Figure Lengend Snippet: Data is processed through a traditional pipeline of RNA-seq data preprocessing and differential expression genes (DEGs) extraction using specific filter parameters such as False Discovery Rate (FDR) <0.05 and fold change (FC) >1.5. The data are utilized to construct a correlation matrix, its correlation heatmap is generated to visualize DEGs’ correlation distribution. For further analysis, the absolute values of the correlations are ordered. The sorted heatmap aids in the visualization of the top genes. Clustering is performed using Dendrogram, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE) methods, followed by distance thresholding (for the Dendrogram results) or K-means (for the PCA and t-SNE results) for finer clustering. Clusters were analyzed via STRING or network analysis to identify potential target genes. All pathways, including generated and existing ones from databases like GO and KEGG, are quantitatively compared using novel indices and ranked for relevance.

Article Snippet: Our implemented t-distributed Stochastic Neighbor Embedding (t-SNE) toolbox that performs t-SNE calculations ( ) uses the results from the correlation matrix and presents the genes based on their correlation values in 3D using a built-in ‘ tsne ’ function implemented in MATLAB.

Techniques: RNA Sequencing Assay, Expressing, Extraction, Construct, Generated