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Transnetyx
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Transnetyx
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Broad Institute Inc
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KNIME GmbH
workflow for the pre-processing and the analysis of the data Workflow For The Pre Processing And The Analysis Of The Data, supplied by KNIME 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/workflow+process/pmc07545555-71-1-14?v=KNIME+GmbH Average 90 stars, based on 1 article reviews
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BioTherapeutics Inc
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Seamap International Holdings Pte Ltd
australia data processing workflow ![]() Australia Data Processing Workflow, supplied by Seamap International Holdings Pte Ltd, 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/workflow+process/pmc06624293-169-3-2?v=Seamap+International+Holdings+Pte+Ltd Average 90 stars, based on 1 article reviews
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KNIME GmbH
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Celera
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KNIME GmbH
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KNIME GmbH
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KAUST Core Labs
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INFINIUM Inc
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Image Search Results
Journal: Scientific Data
Article Title: A seafloor habitat map for the Australian continental shelf
doi: 10.1038/s41597-019-0126-2
Figure Lengend Snippet: Seamap Australia data processing workflow.
Article Snippet: Fig. 1
Techniques:
Journal: Frontiers in computer science
Article Title: Integration of the ImageJ Ecosystem in the KNIME Analytics Platform
doi: 10.3389/fcomp.2020.00008
Figure Lengend Snippet: Side-by-side comparison of ImageJ macro with KNIME workflow using KNIME Image Processing nodes.
Article Snippet: Using
Techniques: Comparison
Journal: Frontiers in computer science
Article Title: Integration of the ImageJ Ecosystem in the KNIME Analytics Platform
doi: 10.3389/fcomp.2020.00008
Figure Lengend Snippet: Quantitative analysis of subcellular structures. To analyze the consequences of disrupting the cytoskeleton on matrix adhesion focal adhesion complex formation is visualized using TIRF imaging. A KNIME workflow was leveraged to define the number of focal adhesion complexes at the periphery of the cell vs. within the center of the cell using sequential steps with discrete objectives: I. Read file in, II. Processing of the images into labels that capture the individual focal adhesion complexes, III. Arithmetic on the labels to obtain the characteristics (features) for classification, IV. Classification of the labels using extracted features, V. Visualization of the classified focal adhesion complexes (periphery vs center). Note that the output of this pipeline is a combination of visualization and quantitation. Both of these can be leveraged for further analysis.
Article Snippet: Using
Techniques: Imaging, Quantitation Assay
Journal: Frontiers in computer science
Article Title: Integration of the ImageJ Ecosystem in the KNIME Analytics Platform
doi: 10.3389/fcomp.2020.00008
Figure Lengend Snippet: Quantitative analysis of histological stain. The histological staining of a cell adhesion marker (CD166) related to tumor invasion and metastasis demonstrates significant variation across patient samples. As in user case #1, the KNIME workflow was divided into sequential steps that complete discrete objectives: 1) read in file , 2) pre-processing of the images and their annotation in preparation for analysis using ImageJ2 functionalities, 3) pixel classification using Weka-bases machine learning functionality, 4) post-classification processing of image data to labels that correspond to ‘positive’, 5) compilation of labels, images and annotations, 6) visualization of the quantitation by overlaying the labels with the original image.
Article Snippet: Using
Techniques: Staining, Marker, Quantitation Assay
Journal: Frontiers in computer science
Article Title: Integration of the ImageJ Ecosystem in the KNIME Analytics Platform
doi: 10.3389/fcomp.2020.00008
Figure Lengend Snippet: A KNIME workflow for channel-shift correction and particle tracking. The positions of bead detections are shown in three-pane scatter plots, before (left) and after (right) applying channel-shift correction. The density plots show absolute distances between apparent bead locations of two channels before (red) and after (cyan) correction.
Article Snippet: Using
Techniques:
Journal: Nucleic Acids Research
Article Title: Low-input and single-cell methods for Infinium DNA methylation BeadChips
doi: 10.1093/nar/gkae127
Figure Lengend Snippet: Infinium BeadChip performance in ultra-low input ranges. ( A ) A summary table of workflows used in this study. Workflow A is the Illumina standard workflow. ( B ) Box plots were used to visualize the probe success rates (top) and the F1 score (bottom). The number of samples for each experiment was displayed next to each box. (C, D) Comparison of four main preparation methods based on ( C ) probe success rate and ( D ) F1 score. The number on the top right corner of each tile indicates the number of samples analyzed in each experiment. See also . (E, F) Smooth scatterplots for the comparison of workflows A, C, J, and M with ( E ) 2 ng and ( F ) 0.5 ng of DNA input ( R : spearman's rho, P : P -value). The dashed squares indicate intermediate beta values (0.25–0.75) on both axes.
Article Snippet: PGCs are typically present in low numbers, hindering their analysis by the standard
Techniques: Comparison