Educators:
TDA

Date:
Thursday October 8, 2020
6 PM - 7 PM UTC +2 (Berlin)

Location:
Online, https://www.knime.com/about/events/integrated-deployment-in-action-end-to-end-data-science-for-bioactivity-prediction-oct-8-2020

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
alexander.fillbrunn@uni-konstanz.de