Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge
Research output: Contribution to journal › Article
|State||Published - 27 Jun 2018|
|MoE publication type||A1 Journal article-refereed|
- University of Helsinki
- Institute for Molecular Medicine Finland FIMM
- National Institute for Health and Welfare
Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach.
Availability and implementation: Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine.
Supplementary information: Supplementary data are available at Bioinformatics online.