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

doi:10.1093/bioinformatics/bty257

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

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

11 Sitaatiot (Scopus)

170 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Sivut	i395-i403
Julkaisu	Bioinformatics
Vuosikerta	34
Numero	13
DOI - pysyväislinkit	https://doi.org/10.1093/bioinformatics/bty257
Tila	Julkaistu - 27 kesäk. 2018
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

Pääsy asiakirjaan

10.1093/bioinformatics/bty257

bty257Final published version, 405 KBLisenssi: CC BY-NC

Co-Adaptation - Yhteismukauttaminen
Kaski, S. (Vastuullinen tutkija), Kangas, J.-K. (Projektin jäsen), Micallef, L. (Projektin jäsen), Niinimäki, T. (Projektin jäsen), Celikok, M. M. (Projektin jäsen) & Eranti, P. (Projektin jäsen)
01/01/2017 → 31/12/2018
Projekti: Academy of Finland: Other research funding
Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. (Vastuullinen tutkija) & Filstroff, L. (Projektin jäsen)
01/01/2016 → 31/08/2021
Projekti: Academy of Finland: Other research funding
Datalähtöinen päätöksenteko digitaalisessa terveydenhuollossa
Kaski, S. (Vastuullinen tutkija), Vuollekoski, H. (Projektin jäsen), Strahl, J. (Projektin jäsen), Niinimäki, T. (Projektin jäsen), Sundin, I. (Projektin jäsen), Blomstedt, P. (Projektin jäsen), Hegde, P. (Projektin jäsen), Daee, P. (Projektin jäsen) & Eranti, P. (Projektin jäsen)
01/01/2016 → 30/06/2018
Projekti: Academy of Finland: Other research funding

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Probabilistic user modelling methods for improving human-in-the-loop machine learning for prediction
Marttinen, P., Kaski, S., Vehtari, A. & Havulinna, A.
12/05/2021
1 kohde/ Medianäkyvyys
Lehdistö/media: Esiintyminen mediassa
Methods for probabilistic modeling of knowledge elicitation for improving machine learning predictions
Kaski, S.
04/12/2020
1 kohde/ Medianäkyvyys
Lehdistö/media: Esiintyminen mediassa

Siteeraa tätä

Sundin, I., Peltola, T., Micallef, L., Afrabandpey, H., Soare, M., Majumder, M. M., Daee, P., He, C., Serim, B., Havulinna, A., Heckman, C., Jacucci, G., Marttinen, P., & Kaski, S. (2018). Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge. Bioinformatics, 34(13), i395-i403. https://doi.org/10.1093/bioinformatics/bty257

@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 Baris Serim and Aki Havulinna and Caroline Heckman and Giulio Jacucci and Pekka Marttinen and Samuel Kaski",

year = "2018",

month = jun,

day = "27",

doi = "10.1093/bioinformatics/bty257",

language = "English",

volume = "34",

pages = "i395--i403",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "13",

}

Sundin, I, Peltola, T, Micallef, L, Afrabandpey, H, Soare, M, Majumder, MM, Daee, P, He, C, Serim, B, Havulinna, A, Heckman, C, Jacucci, G , Marttinen, P & Kaski, S 2018, 'Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge', Bioinformatics, Vuosikerta 34, Nro 13, Sivut i395-i403. https://doi.org/10.1093/bioinformatics/bty257

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, Baris

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

SN - 1367-4803

VL - 34

SP - i395-i403

JO - Bioinformatics

JF - Bioinformatics

IS - 13

ER -

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

Abstrakti

YK:n kestävän kehityksen tavoitteet

Pääsy asiakirjaan

Sormenjälki

Projektit

Co-Adaptation - Yhteismukauttaminen

Interaktiivinen koneoppiminen useista biodatalähteistä

Datalähtöinen päätöksenteko digitaalisessa terveydenhuollossa

Laitteet

Science-IT

Lehtileikkeet

Probabilistic user modelling methods for improving human-in-the-loop machine learning for prediction

Methods for probabilistic modeling of knowledge elicitation for improving machine learning predictions

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