Explore Scientific Data Stored on BigQuery using KNIME

This example shows how KNIME Analytics Platform can be used to explore and analyze data stored in Google BigQuery.

Data used for the example workflow:
SciWalker Open Data is a comprehensive resource that contains chemistry related data like molecules, nucleotides and peptide sequences (overall 211 million unique molecules) that are linked to additional scientific information. The datasets also include clinical and drug related data with links to different ontologies, which allow us to compare data coming from different data sources using different wording is included.

The data can be explored using the workflow following 5 main steps where user action is needed like selecting data in order to go to the next step:
Step 1) Type a disease into the autocomplete text field and select one
Step 2) Select one compound from the list
Step 3) Select a compound class from the bar chart
Step 4) Select a disease from the Word Cloud
Step 5) Explore the list of compounds that were tested for the diseases selected in 1) and 4)

To open the interactive views, right click the component and select "Execute & Open Views" or select Interactive View if component is already executed.

Note: you will need Google BigQuery credentials to run this workflow.

External resources

• Interactive exploration and analysis of scientific datasets using Google BigQuery and KNIME Analytics Platform
• Tutorial: Importing Bike Data from Google BigQuery
• SciWalker Open Data