BIOME - Core: Computational Biomedicine

Computational Biomedicine

Lectures and research topics of the 3-year curriculum: Modern biomedicine is shaped by novel, complex experimental methods, generating masses of data that can no longer be analysed by traditional means. Progress in biomedicine now critically depends on advanced computational methods, turning bioinformatics into a key area of biomedical research and technology. Symmetrically, biomedicine provides motivating novel problem areas for computational scientists.

The BIOME core "Computational Biomedicine" addresses a spectrum of topics that share two features: computational methods and their applications to biomedical problems. Computational methods cover statistics, algorithm design, machine learning (e.g., clustering and classification methods), discrete and continuous optimisation techniques, biomolecular modeling and others. Biomedical applications can be technological in nature, e.g. development of methods for the analysis of deep sequencing data or high-density oligonucleotide microarray as used for genome-wide association studies, or specifically addressing concrete biomolecular questions, e.g. the role of calcium ions in a class of proteins.

This BIOME core aims at helping PhD students to obtain a comprehensive overview of computational techniques in biomedicine, and to expose their own work to the suggestions and critical questions of their peers and experienced scientists.