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04.1. Integrated Deployment

Integrated Deployment

"Integrated Deployment" exercise for the advanced Life Science User Training
- capture the data preprocessing
- capture the model prediction
- deploy the captured workflow

Activity I: Create Model for Deployment - capture the preprocessing of your data and the model prediction using the Capture Workflow Start and Capture Workflow End nodes - combine your captured workflows using the Workflow Combiner - write/deploy your workflow using the Workflow Writer node Integrated DeploymentThis workflow demonstrates integrated deployment for a Random Forest Model for a bioactivity data set using binary fingerprints.The data set represents a subset of 844 compounds evaluated for activity against CDPK1. 181 compounds inhibited CDPK1 with IC50 below 1uM and have "active" as their class.More information is available https://www.ebi.ac.uk/chemblntd/#tcams_dataset. See Set 19. Step 1: Capture Preprocessing- use the Capture Workflow Start nodewith data port types to start capturing yourpreprocessing part of the workflow- use the RDKit Fingerprint node tocreate binary fingerprints. - use the Capture Workflow End nodewith data port types to end yourpreprocessing part of the workflow Step 3: Combine capturedWorkflows and Deploy- use the Workflow Combinernode to combine thepreprocessing workflow and themodel prediction workflow (HINT:the first part needs to beconnected to the upper inputport)- use the Workflow Writer nodeto deploy your workflow (name:04.2. deployed workflow, outputlocation should be in theExercises folder) Step 2: Capture Prediction Model- use the Capture Workflow Start node with data port typesto start capturing your prediction model- use the Random Forest Predictor node for your modelpredictions- use the Column Filter node to remove your binaryfingerprint column - use the Capture Workflow End node with data port typesto end your prediction model TCAMS_CDPK1_subset_ML.tableNode 348Node 349Node 350Node 351Node 352Node 353Node 354Node 355Node 356Node 357Node 358 Partitioning Random ForestLearner Table Reader CaptureWorkflow Start CaptureWorkflow End RDKit Fingerprint Table Reader CaptureWorkflow End Random ForestPredictor CaptureWorkflow Start Column Filter Workflow Combiner Workflow Writer Scorer Activity I: Create Model for Deployment - capture the preprocessing of your data and the model prediction using the Capture Workflow Start and Capture Workflow End nodes - combine your captured workflows using the Workflow Combiner - write/deploy your workflow using the Workflow Writer node Integrated DeploymentThis workflow demonstrates integrated deployment for a Random Forest Model for a bioactivity data set using binary fingerprints.The data set represents a subset of 844 compounds evaluated for activity against CDPK1. 181 compounds inhibited CDPK1 with IC50 below 1uM and have "active" as their class.More information is available https://www.ebi.ac.uk/chemblntd/#tcams_dataset. See Set 19. Step 1: Capture Preprocessing- use the Capture Workflow Start nodewith data port types to start capturing yourpreprocessing part of the workflow- use the RDKit Fingerprint node tocreate binary fingerprints. - use the Capture Workflow End nodewith data port types to end yourpreprocessing part of the workflow Step 3: Combine capturedWorkflows and Deploy- use the Workflow Combinernode to combine thepreprocessing workflow and themodel prediction workflow (HINT:the first part needs to beconnected to the upper inputport)- use the Workflow Writer nodeto deploy your workflow (name:04.2. deployed workflow, outputlocation should be in theExercises folder) Step 2: Capture Prediction Model- use the Capture Workflow Start node with data port typesto start capturing your prediction model- use the Random Forest Predictor node for your modelpredictions- use the Column Filter node to remove your binaryfingerprint column - use the Capture Workflow End node with data port typesto end your prediction model TCAMS_CDPK1_subset_ML.tableNode 348Node 349Node 350Node 351Node 352Node 353Node 354Node 355Node 356Node 357Node 358 Partitioning Random ForestLearner Table Reader CaptureWorkflow Start CaptureWorkflow End RDKit Fingerprint Table Reader CaptureWorkflow End Random ForestPredictor CaptureWorkflow Start Column Filter Workflow Combiner Workflow Writer Scorer

Nodes

Extensions

Links

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
On NodePit since: 2022-05-17
Last update: 2024-12-17
Created with KNIME version: v4.5.2
Tags: machine learningeducationintegrated deploymentlife science

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