Helmholtz Munich | Kristian Unger
Translational Bioinformatics
In-depth characterization of oncological cohorts and model systems by multi-big data bulk and single-cell omics. Regression and deep learning based prognostic models.
In-depth characterization of oncological cohorts and model systems by multi-big data bulk and single-cell omics. Regression and deep learning based prognostic models.
About our Research
Translational bioinformatics: In-depth characterization of oncological cohorts and model systems by multi-big data bulk and single-cell omics. Regression and deep learning based prognostic models.
The Translational Bioinformatics Group applies and develops biocomputational approaches for the prediction of clinical outcome and the investigation of the mechanisms driving oncological diseases.
- We design and conduct multi-omics studies on clinical cohorts and model systems for patient stratification, functional subtyping, and defining prognostic models
- We use regression and neural network-based machine learning to build clinical outcome prediction models
- We use a wide range of bioinformatics approaches for bulk, spatial, and single-cell omics data to deeply characterize tumors and their microenvironments, and to decipher heterogeneity within and between tumors.
- We collaborate closely with clinical partners on research towards precision oncology with a focus on glioblastoma and head and neck cancer
Staff at Translational Bioinformatics
