Technical University of Munich, Germany
On major obstacle in current medicine and drug development is inherent in the way we define and approach diseases. Here, we will discuss the diagnostic and prognostic value of (multi-)omics panels in general. We will have a closer look at breast cancer subtyping and treatment outcome, as case example, using gene expression panels – and we will discuss the current “best practice” in the light of critical statistical considerations. Afterwards, we will introduce computational approaches for network-based medicine. We will discuss novel developments in graph-based machine learning using examples ranging from Huntington’s disease mechanisms via lung cancer drug target discovery back to where we started, i.e. breast cancer subtyping and treatment optimization – but now from a systems medicine point of view. We conclude that systems medicine and modern artificial intelligence open new avenues to shape future medicine.
Related paper: De novo pathway-based biomarker identification.
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