Online
 
Educators:
TDA
 
Date:
Thursday October 8, 2020
6 PM - 7 PM UTC +2 (Berlin)
 
Location:
 
Contents:
During this 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.
For this, we will demonstrate a use case of bioactivity prediction.
We will:
• Train and optimize four different machine learning methods (Naive Bayes, Logistic Regression, Random Forest, XGBoost)
• Identify the best model to predict the activity of a compound on a particular biological target
• Use KNIME’s new integrated deployment functionality to automatically deploy the best model
• Create a simple web application that uses the deployed model to predict the activity of new compounds
The webinar will round off with a Q&A session. We look forward to lots of questions!
 
Learning goals:
Using Machine Learning for bioactivity prediction
 
Prerequisites:
None
 
Keywords:
Cheminformatics, Bioactivity Prediction, KNIME
 
Tools:
KNIME
 
Contact:
Alexander Fillbrunn
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