Module Number

BIOINF4382 (entspricht BIO-4382) 		Module Title

Machine Learning for Single Cell Biology 		Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral Test
Content

Single-cell technologies in conjunction with machine learning approaches are
transforming the life sciences and the understanding of complex diseases like
cancer. This lecture provides an introduction into (1) the biological and medical
questions that can be uniquely addressed by such single-cell approaches, (2)
state-of-the-art single-cell technologies such as high dimensional mass-/flow
cytometry, multi-omic and/or spatial single-cell sequencing/imaging, and (3)
(un-)supervised machine learning and dynamic modeling approaches to address
afore questions on the basis of high dimensional single-cell data.
Objectives

• Overview state-of-the-art single-cell technologies
• Translation of biological/medical research questions into machine learning problems
• Unsupervised/Supervised/Weakly-supervised machine learning models for characterization of cellular composition of tissues and their association with health/disease states
• Dynamic models for cellular systems
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Claassen
Literature

Programmierkenntnisse Python
Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS, ML-DIV