Altuna Alkalin, Verdan Franke, Bora Uyar, Jan Dohmen, Artem Baranovsky (RBC/deNBI-epi Scientists from Berlin)
August - September 2021
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.
This will be an online training event which will be mostly asynchronous. A typical module would comprise of lectures followed by hands-on exercises and a quiz. The participants will have a week to complete the lectures and exercises for each module at their own pace and at the time of their choosing within that week. Only the participants who complete the exercises and a quiz in a timely manner and have at least 50% of the tasks in the exercises will be invited to the capstone project. The capstone project tasks are designed using data from a real world problem. The participants who provide the best reports for the capstone projects will be invited to co-author a manuscript with the Akalin lab.
- Module 1: Statistics for genomics
- Module 2: Unsupervised learning and applications in genomics
- Module 3: Supervised learning and applications in genomics
- Module 4: Capstone project: Drug response prediction using genomic data
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.r
Some statistics and R programming experience will be good to keep up with the course. Practicals will be done in R.
Computational genomics, RNA-seq, Machine learing,
Application Deadline: 30th of June
More information and application under: https://compgen.mdc-berlin.de/