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MMIV and Machine learning seminar – Parkinson disease: can there be order from chaos?
February 20 @ 14:00 - 16:00
Welcome to this joint MMIV and machine learning seminar! It’s the second MMIV seminar and the eight meeting in our series of machine learning seminars organized as a collaboration between HVL, UiB and HUS.
The machine learning seminar series aims to create an informal platform where people interested in machine learning can meet and discuss across institutions and disciplines. Experienced users, newcomers, and everyone in between are welcome to participate. In this way, we can share experiences, learn from each other and maybe develop a foundation for future collaborations.
We’re very happy to host Charalampos (Haris) Tzoulis, MD, PhD. Haris is a neurologist at Haukeland University Hospital and leads the Neuromics lab at the University of Bergen. He is a highly productive researcher and widely featured in the popular press. For example on NRK Radio and TV, and much much much more.
Speaker: Charalampos Tzoulis, MD, PhD, HUS and UiB
Time: Wednesday, February 20th, 2019
Place: Haukeland Universitetssjukehus, Bikuben
Parkinson disease affects ~1.8% of the population above 65 years and its prevalence is increasing as the population ages. Having no understanding of the mechanisms involved, we are unable to develop therapies and patients suffer progressive disability and premature death. Major bottlenecks hindering breakthroughs in PD research are the disorder’s complexity and heterogeneity. Available evidence suggests that the heterogeneous clinical syndrome we label as PD consists of several molecular subclasses of disease. While some molecular mechanisms are shared across subclasses (convergent mechanisms), others are subclass-specific and contribute to the disorder’s clinicopathological heterogeneity (divergent mechanisms). In order to understand and combat PD, we need to elucidate the molecular signatures associated with these mechanisms and reclassify the disorder in a biologically meaningful manner reflecting the underlying molecular pathogenesis. To achieve this, we propose a systems-approach to characterize and integrate the “ParkOme”: a high-resolution molecular map of PD (and other parkinsonisms) combining multi-omics at unprecedented resolution.