Molecular precision medicine builds on technological advances that enable the profiling of genomes, epigenomes, transcriptomes, proteomes, and metabolomes in a high throughput manner. Analyzing and interpreting such data requires adequate working knowledge in bioinformatics and applied statistics. This module will develop the necessary skills and knowledge to interrogate large datasets.
In Module 6, we will discuss the relevance and role of computational methods in molecular precision medicine. We will develop our programming skills in a widely used programming language, familiarize ourselves with the basic concepts and tools of applied statistics needed to analyze biomedical data, and examine how high-throughput data are analyzed and interpreted in the context of molecular precision medicine.
We will learn the importance of good practice in computational research, including reproducibility and open science. We will develop the autonomy to become self-sufficient in the analysis of biomedical datasets such as those that might be encountered in the Master thesis, while at the same time learn to work effectively in interdisciplinary teams that include data producers and data analysts.
This module will feature a combination of traditional lectures, computational exercises and group work in seminars. We will learn how other people have done it with regular journal clubs, reproduce what other people have done in practice, and develop our own data science methodology. In the final part of this module, we will learn about pharmacoinformatics and structure-based drug design.
Teaching faculty include researchers from the Max Perutz Labs, the Medical University of Vienna, the University of Vienna, the Austrian Academy of Sciences (CeMM Research Center for Molecular Medicine; IMBA - Institute for Molecular Biotechnology), and guest lecturers from leading national and international research institutions.