Nonlinear data fusion over Entity-Relation graphs for Drug-Target Interaction prediction.

ABSTRACT:

Motivation

The prediction of reliable Drug-Target Interactions (DTIs) is a key task in computer-aided drug design and repurposing. Here, we present a new approach based on data fusion for DTI prediction built on top of the NXTfusion library, which generalizes the Matrix Factorization paradigm by extending it to the nonlinear inference over Entity-Relation graphs.

Results

We benchmarked our approach on five datasets and we compared our models against state-of-the-art methods. Our models outperform most of the existing methods and, simultaneously, retain the flexibility to predict both DTIs as binary classification and regression of the real-valued drug-target affinity, competing with models built explicitly for each task. Moreover, our findings suggest that the validation of

SUBMITTER: Mazzone E 

PROVIDER: S-EPMC10265447 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature