Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2673
Title: Drug response prediction by ensemble learning and drug-induced gene expression signatures
Authors: Tan, Mehmet
Özgül, Ozan Fırat
Bardak, Batuhan
Ekşioğlu, Işıksu
Sabuncuoğlu, S.
Keywords: Drug signatures
cell line signatures
drug response prediction
ensemble learning
Issue Date: Sep-2019
Publisher:  Academic Press Inc.
Source: Tan, M., Özgül, O. F., Bardak, B., Ekşioğlu, I., and Sabuncuoğlu, S. (2019). Drug response prediction by ensemble learning and drug-induced gene expression signatures. Genomics, 111(5), 1078-1088.
Abstract: Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in addition to cytotoxicity databases. These large sets of data provided an opportunity to apply machine learning methods to predict drug activity. However, due to the complexity of cancer drug mechanisms, none of the existing methods is perfect. In this paper, we propose a novel ensemble learning method to predict drug response. In addition, we attempt to use the drug screen data together with two novel signatures produced from the drug-induced gene expression profiles of cancer cell lines. Finally, we evaluate predictions by in vitro experiments in addition to the tests on data sets. The predictions of the methods, the signatures and the software are available from http://mtan.etu.edu.tr/drug-response-prediction/. © 2018 Elsevier Inc.
URI: https://www.sciencedirect.com/science/article/pii/S0888754318302416?via%3Dihub
https://hdl.handle.net/20.500.11851/2673
ISSN: 0888-7543
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

9
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

12
checked on Sep 24, 2022

Page view(s)

30
checked on Nov 28, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.