Application of transfer learning for cancer drug sensitivity prediction.

ABSTRACT:

Background

In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context.

Results

In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first approach is based on latent variable cost optimization and the second approach considers polynomial mapping between the two da

SUBMITTER: Dhruba SR 

PROVIDER: S-EPMC6309077 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature