Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel.
Methods
; 219: 73-81, 2023 11.
Article
en En
| MEDLINE
| ID: mdl-37783242
ABSTRACT
Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos
/
Neoplasias
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Methods
Asunto de la revista:
BIOQUIMICA
Año:
2023
Tipo del documento:
Article