Large-Scale private adaptive experiments for drug discovery

Start date 01/11/2015
End date The project is closed: 30/06/2016
This project is part of a larger effort to develop algorithms for large-scale experiment design for the problem of automated drug development. Drug compounds are described by complicated models, and their effectiveness can only be ascertained through laboratory, animal and human testing. A large number of drugs and parameters need to be measured in the and it is impossible to test them all. Existing data about different drugs and their effects may also create privacy issues. These may be due to patient privacy or due to the value attached to expensively collected data. This project will primarily implement algorithms for large-scale experiment design, test them and benchmark them. A second goal is to develop an open framework for communicating experimental specifications, data, results and inferred models. In particular, we would like to be able to interface with collaborative databases such as the open toxicity database. This is a collaborative project at Chalmers between the Department of Computer Science and Engineering and the Department of Mathematical Sciences.

Chalmers Area of Advance ICT

Published: Mon 07 Dec 2015. Modified: Tue 08 Dec 2015