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.
Cite
@article{arxiv.1705.03290,
title = {Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation},
author = {Iiris Sundin and Tomi Peltola and Muntasir Mamun Majumder and Pedram Daee and Marta Soare and Homayun Afrabandpey and Caroline Heckman and Samuel Kaski and Pekka Marttinen},
journal= {arXiv preprint arXiv:1705.03290},
year = {2019}
}
Comments
24 pages, 3 figures