Deep Learning for Genomics
Welcome to the Special Course in Machine Learning in Spring 2025! This course is designed to provide practical insights into the potential and limitations of deep learning in genomics. This is done by reading, presenting and discussing impactful papers of the domain. On top of that, we will also provide some practical tasks.
This course will focus on:
- Understanding Deep Learning: The basics of how deep learning works and understanding different neural network architectures.
- Popular Genomic Assays: Understanding the fundamentals of RNA-seq, ChIP-seq, and DNase-seq, and how these datasets can be integrated with deep learning.
- Genomic Applications: Using deep learning for gene expression analysis, splicing predictions, and modelling chromatin activity.
- Limitations: Addressing the challenges and boundaries of deep learning in genomics.
- Interpretability: Techniques for understanding and explaining model predictions in a biological context.
Structure
We do not expect the students to have a strong background in genetics/genomics, so the first couple of lectures will be dedicated to the basics of genomics. After that, lecturers and students will take turns presenting papers, with some seminars dedicated solely to practical work.
Learning environment
NB!: the first seminar will take place on February 11th, 2025!
Prerequisites
We expect you to be comfortable with
- Python
- Basics of machine learning
Organization
Seminars are scheduled on Tuesday 16.15 - 18.00 in 1024 (most current info is on Slack), Delta (Narva mnt 18).
Zoom: link
We use Slack for communications.
Grading
To pass the course, students need to
- Present a paper in the seminars
Contacts
Dzvinka Yarish dzvenymyra-marta.yarish@ut.ee
Kaur Alasoo kaur.alasoo@ut.ee