Versatile and Open Analytics for the Life Sciences
Why KNIME for Life Sciences
Work with your specific life science data types easily and in one single environment.
Manage large amounts of data all in one place.
Take advantage of machine learning capabilities in KNIME.
Draw on expertise from the KNIME Team as well as the community via the KNIME Forum.
Life Sciences on the KNIME Blog
Learn how to use Python code found in Jupyter notebooks in KNIME as well as how to execute KNIME workflows directly from within Python.
User-friendly End-to-End Lab Automation in Action - Learn how KNIME workflows can automatically retrieve and analyze laboratory data.
Variant prioritization - Reproducible Workflow with Domain Expert Interaction. We combine typical bioinformatics command line tools with built-in functionality in KNIME Analytics Platform including shared components, REST Services, and interactive visualizations and make the results available for interactive investigation as Web Application.
FAIR data with KNIME - Use KNIME to transform your data and align it to the FAIR guiding principles.Visit KNIME Blog
KNIME in Action
Examples of KNIME in Action from our community of Life Science users:
Deep Learning: From Mastering the Game of GO to Revolutionizing Microscopy - by Florian Jug (deNBI).
Building a Clinically Significant Rare Disease Data Master: Approach and Workflows - by Sebastien Lefebvre (Alexion Pharmaceuticals).
Using KNIME to Build a Data-Driven Culture (and Workflows!) in a Biopharma Setting - by Kenneth Longo (WAVE Life Sciences).
Workflow Snippets: Model Prediction Chemistry
This workflow snippet demonstrates how to use an already trained model on new data to predict bioactivity. Simply load the trained model with the Model Reader node and provide this to the corresponding Predictor Node, in our case this is the Random Forest Predictor node.Download Snippet from KNIME Hub More Life Science Snippets
Video: Working with the RDKit in KNIME Analytics Platform
Learn what you can do with KNIME and the RDKit, based on a couple of examples of common cheminformatics use cases. In addition to using the RDKit KNIME nodes, learn how you can use the broader functionality available using Python and Java scripting nodes available in KNIME.Watch Now
Video: Conformal Prediction
Learn about a set of nodes, developed by our partner Redfield, for performing conformal prediction, which is an algorithm for making predictions at a user-set confidence level. See the nodes in action in a KNIME workflow.Watch Now
Video: Integrated Deployment in Action: End to End Data Science for Bioactivity Prediction
In this recorded webinar, we will guide you through the complete journey of a data scientist: from training and selecting the best machine learning model for your data to putting your model into production and creating a simple web application.Watch Now
Reduce time spent sifting through medical literature with automatic disease tagging.
Narrowing sets of genes down to the ones of interest.
Extracting and sharing knowledge in a reusable way.