Files and links (2)
Published (Version of record)CC BY-NC V4.0, Open Access
Published (Version of record)CC BY-NC V4.0, Open
Journal article
Kernelized rank learning for personalized drug recommendation
Bioinformatics, Vol.34(16), pp.2808-2816
08/03/2018
PMID: 29528376
Metrics
1 Record Views
Abstract
Motivation: Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (i) medical records only contain the response of a patient to very few drugs, (ii) drugs are recommended by doctors based on their expert judgment and (iii) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties.
Results: We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic.
Availability and implementation: The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning.
Details
- Title
- Kernelized rank learning for personalized drug recommendation
- Creators
- Xiao He - ETH ZurichLukas Folkman - ETH ZurichKarsten Borgwardt - ETH Zurich
- Publication Details
- Bioinformatics, Vol.34(16), pp.2808-2816
- Publisher
- Oxford University Press
- Number of pages
- 9
- Grant note
- This work has been supported by the SNSF Starting Grant ‘Significant Pattern Mining’.
- Identifiers
- 991013341085202368
- Copyright
- © The Author(s) 2018.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article