aalto1 untyped-item.component.html
Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression
Loading...
Files
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Bioinformatics, Volume 33, issue 14, pp. i359-i368
Abstract
Motivation: A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations. Results: We present a novel approach that leverages on systematic integration of data sources to identify response predictive features of multiple drugs. To solve the modeling task we implement a Bayesian linear regression method. To further improve the usefulness of the proposed model, we exploit the known human cancer kinome for identifying biologically relevant feature combinations. In case studies with a synthetic dataset and two publicly available cancer cell line datasets, we demonstrate the improved accuracy of our method compared to the widely used approaches in drug response analysis. As key examples, our model identifies meaningful combinations of features for the well known EGFR, ALK, PLK and PDGFR inhibitors.
Description
Keywords
Other note
Citation
Ammad-Ud-Din, M, Khan, S A, Wennerberg, K & Aittokallio, T 2017, 'Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression', Bioinformatics, vol. 33, no. 14, pp. i359-i368. https://doi.org/10.1093/bioinformatics/btx266