Masters Projects - Further details

Multi-omic data integration of Transcriptomic, Metabolomic, Metaproteomic, Microbiome, and Phenotypic data.

The efficient integration of multi-omic data is the key to gain deep insights into the interplay of biological mechanisms steering complex diseases. The main focus of this project lies in the efficient integration of time-resolved data of 170 inflammatory bowel disease(IBD) patients. This unpublished dataset includes measurements of the following 5 omic levels: Transcriptomic, Metabolomic, Metaproteomic, Microbiome, and Phenotypic data. Therefore, it will be necessary to apply unsupervised machine learning methods like PCA, tSNE, UMAP, and clustering for exploratory data analysis and use supervised machine learning approaches like regression or random forest for data integration. The result of this project will guide the identification of key mechanisms of IBD progression and potential patient treatment.

Contact: christoph.ogrisnoSp@m@helmholtz-muenchen.de