DLP: Duplex Link Prediction Via Subspace Segmentation for Predicting drug-MiRNA Associations.
IEEE/ACM Trans Comput Biol Bioinform
; PP2024 May 20.
Article
em En
| MEDLINE
| ID: mdl-38767995
ABSTRACT
The arduous and costly journey of drug discovery is increasingly intersecting with computational approaches, which promise to accelerate the analysis of bioassays and biomedical literature. The critical role of microRNAs (miRNAs) in disease progression has been underscored in recent studies, elevating them as potential therapeutic targets. This emphasizes the need for the development of sophisticated computational models that can effectively identify promising drug targets, such as miRNAs. Herein, we present a novel method, termed Duplex Link Prediction (DLP), rooted in subspace segmentation, to pinpoint potential miRNA targets. Our approach initiates with the application of the Network Enhancement (NE) algorithm to refine the similarity metric between miRNAs. Thereafter, we construct two matrices by pre-loading the association matrix from both the drug and miRNA perspectives, employing the K Nearest Neighbors (KNN) technique. The DLSR algorithm is then applied to predict potential associations. The final predicted association scores are ascertained through the weighted mean of the two matrices. Our empirical findings suggest that the DLP algorithm outperforms current methodologies in the realm of identifying potential miRNA drug targets. Case study validations further reinforce the real-world applicability and effectiveness of our proposed method. The code of DLP is freely available at https//github.com/kaizheng-academic/DLP.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
ACM Trans Comput Biol Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article