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/.
journal_name
Genomicsjournal_title
Genomicsauthors
Tan M,Özgül OF,Bardak B,Ekşioğlu I,Sabuncuoğlu Sdoi
10.1016/j.ygeno.2018.07.002subject
Has Abstractpub_date
2019-09-01 00:00:00pages
1078-1088issue
5eissn
0888-7543issn
1089-8646pii
S0888-7543(18)30241-6journal_volume
111pub_type
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