Educators:
Altuna Alkalin, Bora Uyar, Artür Manukyan (RBC/deNBI-epi Scientists from Berlin
Date:
Februar - March 2025
Location:
Online
Contents:
The general aim of the course is to equip participants with practical and technical knowledge to deploy machine learning methods on genomic data sets. With this aim in mind, we will go through certain statistical concepts and move on to unsupervised and supervised machine learning methods to analyze high-dimensional data sets.
There will be theoretical lectures followed by practical sessions where students directly apply what they have learned. These sessions will be provided online in succession. Participants will have a week to work on each module in their own time. Interactions will be provided over the online teaching platform. The programming will be mainly done in R and Python. For module 1, no extensive coding experience will be required. Participants can apply to any module, it is not necessary to apply for all the modules. When accepted, students can participate in the modules of their choice.
- Module 1: AI-assisted data analysis (1 week)
- Module 2: Spatial omics data analysis (1 week)
- Module 3: Multi-omics data integration (1 week)
Learning goals:
The course will be beneficial for first year computational biology PhD students, and experimental biologists and medical scientists who want to begin data analysis or are seeking a better understanding of computational genomics and analysis of popular sequencing methods.
Prerequisites:
Some statistics and R programming experience will be good to keep up with the course. Practicals will be done in R.
Keywords:
Computational genomics, RNA-seq, Machine learing,
Tools:
R/Bioconductor
Application Deadline: 30th of January
More information and application under: https://bioinformatics.mdc-berlin.de/compgen/2025/