sreda, 19. december 2018 Vabilo na predavanje dr. Marinke Žitnik »Deep Learning for Network Medicine«
Oddelka za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM vabita
na predavanje dr. Marinke Žitnik (Stanford University, ZDA)
z naslovom »Deep Learning for Network Medicine«,
ki bo v četrtek, 20. decembra 2018, bo ob 13.30
v predavalnici FAMNIT-VP1 (Glagoljaška 8, Koper).
Predavanje bo potekalo v angleškem jeziku.
O predavateljici:
Marinka Žitnik je leta 2012 diplomirala iz računalništva in matematike na Fakulteti za računalništvo in informatiko ter Fakulteti za matematiko in fiziko v Ljubljani. Študij je nadaljevala na ljubljanski Fakulteti za računalništvo in informatiko, kjer je leta 2015 tudi doktorirala. Ukvarjala se je z razvojem algoritmov strojnega učenja, ki gradijo napovedne modele z zlivanjem velikih količin heterogenih podatkov. Razvite tehnike je skupaj s sodelavci uporabila za reševanje aktualnih problemov v bioinformatiki in sistemski biologiji. Dr. Žitnik je tekom študija gostovala na Univerzi v Torontu, Imperial College v Londonu, Baylor College of Medicine v Houstonu in na Univerzi Stanford. Trenutno je postdoktorska raziskovalka na oddelku za računalništvo Univerze Stanford (ZDA).
O predavanju (angl.):
Networks pervade medical research and practice. The primary challenge is how to learn on biomedical networks that involve rich interactions, spanning from the molecular scale all the way to the societal scale encompassing all human interactions. However, prevailing deep learning algorithms are designed for data with a regular, grid-like structure and cannot exploit rich interactions, the essence of biomedical networks.
In this talk, I will discuss methods that learn how to embed nodes in rich biomedical networks as points in a low-dimensional embedding space, where the geometry of this space is optimized to reflect the structure of interactions between the nodes. These embeddings methods are at the technical core of Decagon, the first approach for predicting side effects of drug combinations, not merely of individual drugs. Decagon composes a massive network describing how proteins in our bodies interact with each other and how different drugs affect these proteins. Decagon's deep embedding method uses the network to identify patterns in how side effects arise based on how drugs target different proteins. Today, in many cases, it is unknown what side effects might arise from adding another drug to a patient's personal pharmacy, and Decagon has the potential to lead to more effective and safer healthcare.
Vabljeni!
