Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge

Iiris Sundin, Tomi Peltola, Luana Micallef, Homayun Afrabandpey, Marta Soare, Muntasir Mamun Majumder, Pedram Daee, Chen He, Baris Serim, Aki Havulinna, Caroline Heckman, Giulio Jacucci, Pekka Marttinen, Samuel Kaski

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
148 Downloads (Pure)

Abstract

Motivation Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large.

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.
Original languageEnglish
Pages (from-to)i395-i403
JournalBioinformatics
Volume34
Issue number13
DOIs
Publication statusPublished - 27 Jun 2018
MoE publication typeA1 Journal article-refereed

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