  Anti-cancer drug sensitivity prediction using deep learning models for
individual cell line is a significant challenge in personalized medicine.
REFINED (REpresentation of Features as Images with NEighborhood Dependencies)
CNN (Convolutional Neural Network) based models have shown promising results in
drug sensitivity prediction. The primary idea behind REFINED CNN is
representing high dimensional vectors as compact images with spatial
correlations that can benefit from convolutional neural network architectures.
However, the mapping from a vector to a compact 2D image is not unique due to
variations in considered distance measures and neighborhoods. In this article,
we consider predictions based on ensembles built from such mappings that can
improve upon the best single REFINED CNN model prediction. Results illustrated
using NCI60 and NCIALMANAC databases shows that the ensemble approaches can
provide significant performance improvement as compared to individual models.
We further illustrate that a single mapping created from the amalgamation of
the different mappings can provide performance similar to stacking ensemble but
with significantly lower computational complexity.
