Title: Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation,
Abstract: Predicting the efficacy of a drug for a given individual, using
high-dimensional genomic measurements, is at the core of precision medicine.
However, identifying features on which to base the predictions remains a
challenge, especially when the sample size is small. Incorporating expert
knowledge offers a promising alternative to improve a prediction model, but
collecting such knowledge is laborious to the expert if the number of candidate
features is very large. We introduce a probabilistic model that can incorporate
expert feedback about the impact of genomic measurements on the sensitivity of
a cancer cell for a given drug. We also present two methods to intelligently
collect this feedback from the expert, using experimental design and
multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51),
expert knowledge decreased the prediction error by 8%. Furthermore, the
intelligent approaches can be used to reduce the workload of feedback
collection to less than 30% on average compared to a naive approach.