Hi KNIME community!
I’m reaching out on behalf of the Ardigen team, an AI-powered drug discovery platform that’s bridging bioinformatics and machine learning to accelerate therapeutic development.
Why I’m here:
We’ve been using KNIME extensively for building data integration and modeling workflows, especially in multi-omics analysis pipelines. While Linux-based servers handle heavy compute, we’ve found that Windows workstations, enhanced with WSL2 and Docker, often serve as critical hubs for prototyping and visualization—all seamlessly integrated with KNIME.
Key areas where KNIME plays a role at Ardigen:
- ETL pipelines: Pulling data from diverse sources (genomics, transcriptomics, pharma databases) and preprocessing via KNIME nodes.
- Model orchestration: Using KNIME to chain Python and R-based AI/ML models, then evaluating results directly within the UI.
- Hybrid execution: Testing workflows locally on Windows (with WSL2) before deploying them to Linux compute clusters.
- Visualization & QA: Leveraging KNIME’s interactive views to quickly inspect multi-omics outputs, and handoff to tools like PyMOL or custom Windows dashboards.
I’d love to hear from this group:
- Do any of you use Windows + WSL2 as part of your KNIME pipeline testing or visualization steps?
- Which KNIME extensions (bio, analytics, scripting, etc.) have become indispensable in your life science workflows?
- Any tips on optimizing performance when chaining Python/R scripts into KNIME on hybrid systems?
Looking forward to exchanging ideas and learning about your KNIME setups—especially how you’re blending Windows-based dev environments with robust scientific workflows.
Cheers,