Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction

TL;DR

This paper explores ensemble methods of REFINED CNN models for improved anti-cancer drug sensitivity prediction, demonstrating significant performance gains and computational efficiency over single models.

Contribution

It introduces ensemble strategies for REFINED CNN models that enhance prediction accuracy and reduce computational complexity in drug sensitivity tasks.

Findings

Ensemble approaches outperform individual REFINED CNN models.

A single combined mapping achieves similar performance to stacking ensembles.

Significant performance improvements demonstrated on NCI60 and NCIALMANAC datasets.

Abstract

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.