EESTECH Case Challenge on Preclinical Drug Safety with the TG-GATEs database – application deadline 19.03 at 24h

About the event

This is a case study/hackathon organized by EESTEC Zurich, sponsored by Roche, and happening on the Roche campus in Basel. More information (location and time) will be given to the registered people. Registration deadline is the 19.03 night.

What is it about:
EESTech Challenge is an international hackathon that consists of a local round and a final round. In 24 cities all over Europe EESTEC organizes a local round. All winning teams of the local rounds are invited to Novi Sad (Serbia) to compete in the final round for the title of the EESTech Challenge champion. The topic of this years challenge will be big data.

How to participate:
The Swiss local round takes place on the 23.3.2018 in Basel (transport for people from Zürich will be organized) and it will be conducted as a 12h hackathon. You can participate in teams of three. But what if you don’t manage to find 2 other motivated and skilled people for your team? No worries, you can also apply on your own and will then be assigned to a team with two other equally lovely participants.
The winning team of our local round will qualify for the final round, which will be held in May 2018. Travel to Novi Sad, as well as the accommodation there will be completely free for you.

Further information about the local round task:
Machine Learning for Preclinical Drug Safety: a case study with the TG-GATEs database Drug candidates are comprehensively and thoroughly tested for their safety profiles before they enter clinical trials. Gene expression profiling with omics technologies, often applied in combination with cell-based assays or animal tests, has contributed significantly to our understanding of the safety-relevant findings of drug candidates.

In this contest, we focus on two types of data that are often encountered in preclinical research: drug-induced gene expression data and pathology. The goals of the contest are to create algorithms and software that (1) best predict pathology findings given gene expression profiles, (2) deepen our understanding of the molecular mechanisms underlying the pathology findings the most.

For registration go here.