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2018 ; 34
(13
): i395-i403
Nephropedia Template TP
gab.com Text
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Improving genomics-based predictions for precision medicine through active
elicitation of expert knowledge
#MMPMID29949984
Sundin I
; Peltola T
; Micallef L
; Afrabandpey H
; Soare M
; Mamun Majumder M
; Daee P
; He C
; Serim B
; Havulinna A
; Heckman C
; Jacucci G
; Marttinen P
; Kaski S
Bioinformatics
2018[Jul]; 34
(13
): i395-i403
PMID29949984
show ga
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.