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
Year: 2019
Type: review
Source: Pharmacology & Therapeutics
Authors: Konstantinos Vougas, Theodore Sakellaropoulos, Athanassios Kotsinas, George-Romanos P. Foukas, Andreas Ntargaras, Filippos Koinis, Alexander Polyzos, Vassilios Myrianthopoulos, Hua Zhou, Sonali Narang, Vassilis Georgoulias, Leonidas Alexopoulos, Iannis Aifantis, Paul A. Townsend, Petros Sfikakis, Rebecca Fitzgerald, Dimitris Thanos, Jiri Bartek, Russell Petty, Aristotelis Tsirigos, Vassilis G. Gorgoulis
Institutions: National and Kapodistrian University of Athens, Biomedical Research Foundation of the Academy of Athens, NYU Langone Health, NYU Langone’s Laura and Isaac Perlmutter Cancer Center, New York University, Weill Cornell Medicine, University of Crete, National Technical University of Athens, Manchester Academic Health Science Centre, University of Manchester, NIHR Manchester Biomedical Research Centre, Cancer Research UK Manchester Centre, Laiko General Hospital of Athens, University of Cambridge, MRC Cancer Unit, Hutchison/MRC Research Centre, Medical Research Council, Science for Life Laboratory, Karolinska Institutet, Institute of Molecular and Translational Medicine, Danish Cancer Society, Palacký University Olomouc, University of Dundee, Ninewells Hospital
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Horizon 2020, National Cancer Institute
Award: Cancer Center Support Grant
OpenAlex ID: W2965150010