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

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

Organisaatiot

  • University of Helsinki
  • Institute for Molecular Medicine Finland FIMM
  • National Institute for Health and Welfare

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivuti395-i403
JulkaisuBioinformatics
Vuosikerta34
Numero13
TilaJulkaistu - 27 kesäkuuta 2018
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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