compgen2021 hands-on course on machine learning for genomics

Course url: https://compgen.mdc-berlin.de/ or https://bioinformatics.mdc-berlin.de/compgen/2021/

Organizer: Altuna Akalin (https://bioinformatics.mdc-berlin.de/ + http://al2na.co)

Prerequisites

• Programming with R
• Being able to make reports with Rmarkdown
• Understanding of the basic probability and statistics concepts
• Conceptual understanding of high-throughput assays (sequencing, microarrays etc.) in genomics

Learning Objectives and material

Module 1: Statistics for genomics (2-8 August 2021)

• A simple intro to statistical distributions
• hypothesis testing
• linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

• Understanding basic intuition behind machine learning approaches.
• Using unsupervised learning to cluster and visualise data points
• Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

• Understanding and using supervised learning methods for predictive purposes
• How to measure prediction performance
• Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/