Ensemble transfer learning for the prediction of anti-cancer drug response.

ABSTRACT: Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug developme

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC7581765 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature