  Chemotherapeutic response of cancer cells to a given compound is one of the
most fundamental information one requires to design anti-cancer drugs. Recent
advances in producing large drug screens against cancer cell lines provided an
opportunity to apply machine learning methods for this purpose. In addition to
cytotoxicity databases, considerable amount of drug-induced gene expression
data has also become publicly available. Following this, several methods that
exploit omics data were proposed to predict drug activity on cancer cells.
However, due to the complexity of cancer drug mechanisms, none of the existing
methods are perfect. One possible direction, therefore, is to combine the
strengths of both the methods and the databases for improved performance. We
demonstrate that integrating a large number of predictions by the proposed
method improves the performance for this task. The predictors in the ensemble
differ in several aspects such as the method itself, the number of tasks method
considers (multi-task vs. single-task) and the subset of data considered
(sub-sampling). We show that all these different aspects contribute to the
success of the final ensemble. 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 the method
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
\url{http://mtan.etu.edu.tr/drug-response-prediction/}.
