Journal: bioRxiv

Article Title: Quantification of telomere features in tumor tissue sections by an automated 3D imaging-based workflow

doi: 10.1101/053132

Figure Lengend Snippet: A. Schematic workflow for sequential imaging on different scales. First, regions of interest are identified in a pre-screen. Then, the sample is transferred to a second microscope and after a referencing step the previously identified regions can be addressed and acquired in 3D with higher resolution and more color channels. B. KNIME implementation of the workflow in A. C. Example images. On the left, the single tissue spots of a TMA can be seen. Next to it, the two wide-field images acquired of the first spot are shown. These were processed in order to identify the ROIs as described in the text. On the right side, the same region is shown as imaged by CLSM after sample transfer and referencing (maximum projection of 41 axial layers). D. Schematic workflow for integrated imaging on different scales. In each image of a primary screen, regions of interest are identified. These positions are directly fed back to the microscope and a high-resolution z-stack is acquired for each region. Afterwards, the next scan field of the primary screen is acquired. E. KNIME implementation of the workflow in D. F. Example images. On the left, the single spots of a TMA can be seen. Next to it, a two-color image of the highlighted region of the confocal prescreen is shown. This image is directly processed in order to find ROIs (i.e. cellular structures comprising five or more telomere spots) indicated in random colors. On the right side, one region is shown as imaged by the subsequent 3D screen (maximum projection of 41 axial layers).

Article Snippet: The scan was embedded into a KNIME workflow loop ( ).

Techniques: Imaging, Microscopy

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: Decision Tree model. The left panel illustrates the traditional approach that detects gene-disease associations, while the right panel illustrates the disease-disease association as the output of GediNET.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques:

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: GediNET workflow. The main workflow of G-S-M that integrates pre-existing biological knowledge for grouping genes based on disease-gene association, which is derived from the DisGeNET v7 database.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques: Derivative Assay

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: GediNET workflow in KNIME.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques:

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: An example averages of 100 MCCV performance table of GediNET for top-ranked 10 groups for GDS1962 dataset cumulatively.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques:

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: Performance results of GediNET over the top-ranked group.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques:

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: The mean AUC values of GediNET, CogNet, maTE and PriPath for ten different datasets for the top two groups.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques:

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: The mean number of genes of GediNET, CogNet, maTE and PriPath tools for ten different datasets for the top two groups.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques:

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: An example of the DDA for four datasets in GediNET. The number of shared genes for the top-scored disease group is represented. The upper panel shows the DDA for GDS1962, GDS3257, GDS2771 and GDS5499 datasets. The lower panel shows the annotations used in the DDA illustration formation.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques:

Journal: Scientific Reports

Article Title: GediNET for discovering gene associations across diseases using knowledge based machine learning approach

doi: 10.1038/s41598-022-24421-0

Figure Lengend Snippet: Illustrates the three top detected diseases by DisGeNET API and the top 3 ranked diseases by GediNET for each GEO dataset.

Article Snippet: Figure 6 GediNET workflow in KNIME.

Techniques: Clinical Proteomics

Journal: Journal of Cheminformatics

Article Title: Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

doi: 10.1186/s13321-024-00814-3

Figure Lengend Snippet: KNIME QSAR-ready chemical structure standardization workflow organized by sections of the process

Article Snippet: The QSAR-ready chemical structure standardization workflow in KNIME offers a wide range of features and capabilities that make it an ideal choice for different cheminformatic applications.

Techniques:

Journal: Journal of Cheminformatics

Article Title: Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

doi: 10.1186/s13321-024-00814-3

Figure Lengend Snippet: Input and workflow settings

Article Snippet: The QSAR-ready chemical structure standardization workflow in KNIME offers a wide range of features and capabilities that make it an ideal choice for different cheminformatic applications.

Techniques:

Journal: Journal of Cheminformatics

Article Title: Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

doi: 10.1186/s13321-024-00814-3

Figure Lengend Snippet: Workflow landing page on the KNIME Server

Article Snippet: The QSAR-ready chemical structure standardization workflow in KNIME offers a wide range of features and capabilities that make it an ideal choice for different cheminformatic applications.

Techniques:

Journal: Journal of Cheminformatics

Article Title: Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

doi: 10.1186/s13321-024-00814-3

Figure Lengend Snippet: Workflow configuration options on the KNIME Server. A File input. B Drawing input

Article Snippet: The QSAR-ready chemical structure standardization workflow in KNIME offers a wide range of features and capabilities that make it an ideal choice for different cheminformatic applications.

Techniques:

Journal: AMIA Summits on Translational Science Proceedings

Article Title: A Prototype for Executable and Portable Electronic Clinical Quality Measures Using the KNIME Analytics Platform

doi:

Figure Lengend Snippet: Implementation of CMS30 (Statin at Discharge) on KNIME. Arrow-headed lines denote transmission of data tables; square-headed lines denote database connections; round-headed lines denote transmission of flow variables. Pie chart in lower right is generated from visualization nodes in Region D.

Article Snippet: To test portability, we executed the CMS30 KNIME workflow on both Vanderbilt University (VU) and Northwestern University (NU) data.

Techniques: Transmission Assay, Generated