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
Scale and orchestrate the modeling process to train and evaluate 300,000 models or bioactivity prediction.
Explore, analyze, visualize: create interactive views using sunburst charts, tag clouds, and networks based on the example of investigating disease-related genes.
Learn how to use Python code found in Jupyter notebooks in KNIME as well as how to execute KNIME workflows directly from within Python.
Answer questions from the area of pharmaceutical research by linking and querying different datasets stored in BigQuery.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). Open slides.
Building a Clinically Significant Rare Disease Data Master: Approach and Workflows - by Sebastien Lefebvre (Alexion Pharmaceuticals). Open slides.
Using KNIME to Build a Data-Driven Culture (and Workflows!) in a Biopharma Setting - by Kenneth Longo (WAVE Life Sciences). Open slides.
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: Gene Expression Analysis with KNIME Analytics Platform
In this recorded webinar, we find and annotate differentially expressed genes from tumors as well as matched normal tissue from patients with oral squamous cell carcinomas. For this we use use our favorite R library, extract data from Google’s BigQuery, and use shared components to customize our analysis.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.