Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining - PubMed (original) (raw)

Review

doi: 10.1016/j.pharmthera.2019.107395. Epub 2019 Jul 30.

Theodore Sakellaropoulos 2, Athanassios Kotsinas 3, George-Romanos P Foukas 3, Andreas Ntargaras 3, Filippos Koinis 3, Alexander Polyzos 4, Vassilios Myrianthopoulos 5, Hua Zhou 6, Sonali Narang 2, Vassilis Georgoulias 7, Leonidas Alexopoulos 8, Iannis Aifantis 2, Paul A Townsend 9, Petros Sfikakis 10, Rebecca Fitzgerald 11, Dimitris Thanos 12, Jiri Bartek 13, Russell Petty 14, Aristotelis Tsirigos 15, Vassilis G Gorgoulis 16

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Review

Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining

Konstantinos Vougas et al. Pharmacol Ther. 2019 Nov.

Abstract

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.

Keywords: Association Rule Mining; Data mining; Drug Response Prediction; Machine Learning; Precision Medicine.

Copyright © 2019 Elsevier Inc. All rights reserved.

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