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

Tutkimustuotos: Lehtiartikkeli

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Bibtex - Lataa

@article{e381d3411e2345f6aff93b385039b21a,
title = "Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge",
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.",
author = "Iiris Sundin and Tomi Peltola and Luana Micallef and Homayun Afrabandpey and Marta Soare and Majumder, {Muntasir Mamun} and Pedram Daee and Chen He and Bariş Serim and Aki Havulinna and Caroline Heckman and Giulio Jacucci and Pekka Marttinen and Samuel Kaski",
year = "2018",
month = "6",
day = "27",
doi = "10.1093/bioinformatics/bty257",
language = "English",
volume = "34",
pages = "i395--i403",
journal = "Bioinformatics",
issn = "1367-4803",
number = "13",

}

RIS - Lataa

TY - JOUR

T1 - Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge

AU - Sundin, Iiris

AU - Peltola, Tomi

AU - Micallef, Luana

AU - Afrabandpey, Homayun

AU - Soare, Marta

AU - Majumder, Muntasir Mamun

AU - Daee, Pedram

AU - He, Chen

AU - Serim, Bariş

AU - Havulinna, Aki

AU - Heckman, Caroline

AU - Jacucci, Giulio

AU - Marttinen, Pekka

AU - Kaski, Samuel

PY - 2018/6/27

Y1 - 2018/6/27

N2 - 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.

AB - 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.

U2 - 10.1093/bioinformatics/bty257

DO - 10.1093/bioinformatics/bty257

M3 - Article

VL - 34

SP - i395-i403

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 13

ER -

ID: 21616109