Article added to library!
x
Pubchase is a service of protocols.io - free, open access, crowdsourced protocols repository. Explore protocols.
Sign in
Reset password
or connect with
Facebook
By signing in you are agreeing to our
Terms Of Service and Privacy Policy
Sep 09, 2015
Scientific Reports
The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current study, eigenvalue transformation also has the potential to be applied to other algorithms based on kernels.

Downloading PDF to your library...

Uploading PDF...

PDF uploading

Delete tag:

The link you entered does not seem to be valid

Please make sure the link points to nature.com contains a valid shared_access_token