NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network.
J Cell Mol Med
; 28(7): e18224, 2024 04.
Article
de En
| MEDLINE
| ID: mdl-38509739
ABSTRACT
Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Conception de médicament
/
Langue:
En
Journal:
J Cell Mol Med
Sujet du journal:
BIOLOGIA MOLECULAR
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine