Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment

We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. EVONANO includes a simulator to grow tumours, extract representative scenarios, and then simulate nanoparticle transport through these scenarios to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate our platform with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments.

Model Exploration with Cost-Aware Learning

We present an extension to active learning routines in which non-constant costs are explicitly considered. This work considers both known and unknown costs and introduces the term \epsilon-frugal for learners that do not only consider minimizing total costs but are also able to explore high cost regions of the sample space. We demonstrate our extension on a well-known machine learning dataset and find that out \epsilon-frugal learners outperform both learners with known costs and random sampling.