A2 Refereed review article in a scientific journal
Machine learning and feature selection for drug response prediction in precision oncology applications
Authors: Ali Mehreen, Aittokallio Tero
Publisher: World Scientific Publishing Co. Pte Ltd.
Publication year: 2019
Journal: Biophysical Reviews
Journal name in source: Biophysical reviews
Journal acronym: Biophys Rev
Volume: 11
Issue: 1
First page : 31
Last page: 39
ISSN: 1867-2450
DOI: https://doi.org/10.1007/s12551-018-0446-z(external)
Web address : https://link.springer.com/article/10.1007/s12551-018-0446-z(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/39494736(external)
In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input "big data" require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses.
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