FLONE: fully Lorentz network embedding for inferring novel drug targets.

ABSTRACT:

Motivation

To predict drug targets, graph-based machine-learning methods have been widely used to capture the relationships between drug, target and disease entities in drug-disease-target (DDT) networks. However, many methods cannot explicitly consider disease types at inference time and so will predict the same target for a given drug under any disease condition. Meanwhile, DDT networks are usually organized hierarchically carrying interactive relationships between involved entities, but these methods, especially those based on Euclidean embedding cannot fully utilize such topological information, which might lead to sub-optimal results. We hypothesized that, by importing hyperbolic embedding specifically for modeling hierarchical DDT networks, graph-based algorithms could better

SUBMITTER: Yue Y 

PROVIDER: S-EPMC10235194 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature