Inverse similarity and reliable negative samples for drug side-effect prediction.

ABSTRACT:

Background

In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement.

Methods

Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drug

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC7402513 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature