570005 WS Methods in Bioinformatics (2022W)

This course on PhD candidate level requires substantial knowledge in computational biology and bioinformatics from your master studies. In this course we focus on state-of-the art methods for omics data analysis (e.g. meta/genomics, meta/transcriptomics, meta/proteomics), integration of heterogeneous data, statistical analysis and machine learning, genome-scale modeling. We address all areas of life as well as viruses. Participants will learn basic concepts of machine learning, including an introduction to the Python data science stack, as well as several specific methods and evaluation strategies. The lectures are supplemented by practical examples and discussions on current literature in the field of applied machine learning for biological problems. After the course, participants will be able to decide, whether a given biological problem can be tackled with machine learning. Further, participants will be able to assess the quality of machine learning approaches in the scientific literature. We welcome students to bring their projects to the course, as these can provide starting points for practical experiments in the course. For the entire course we will work on notebooks and on the Life Science Compute Cluster.