ML4Omics
Class: ML4Omics
Titel:
Introduction to Machine Learning for Omics Data Analysis
Duration:
Wintersemester 2026/2027
Description:
The goal of the lecture, hold within the RTG2978, is to understand the basics of Machine Learning, apply simple methods to OMICs data (e.g. genomics, proteomics, metabolomics), and interpret the results. The focus is on understanding what Machine Learning is, how it works, and how it can be applied to OMICs data.
The lecture aims to enable participants to:
1. Understand the basics of Machine Learning, such as:
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What is Machine Learning??
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How does Machine Learning work?
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What types of Machine Learning exist (e.g. supervised, unsupervised, deep learning)?
Apply simple Machine Learning methods to OMICs data, such as:
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Classification of genes or proteins
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Regression of gene expression data
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Clustering of metabolomics data
Interpret the results of Machine Learning, such as:
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How can I interpret the results of a Machine Learning model?
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How can I assess the performance of a machine learning model and critically evaluate its ability to generalize and provide meaningful insights?
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How can I interpret the results of a Machine Learning model in a biological context?
By participating in this lecture, participants will be able to apply Machine Learning to OMICs data and interpret the results, thereby improving their research and work in the fields of biology, medicine, and bioinformatics.