MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding.

ABSTRACT:

Background

In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously.

Results

We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classif

SUBMITTER: Binatlı OC 

PROVIDER: S-EPMC10324162 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature