L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions.
BMC Bioinformatics
; 20(Suppl 8): 287, 2019 Jun 10.
Article
en En
| MEDLINE
| ID: mdl-31182006
ABSTRACT
BACKGROUND:
Predicting drug-target interactions is time-consuming and expensive. It is important to present the accuracy of the calculation method. There are many algorithms to predict global interactions, some of which use drug-target networks for prediction (ie, a bipartite graph of bound drug pairs and targets known to interact). Although these algorithms can predict some drug-target interactions to some extent, there is little effect for some new drugs or targets that have no known interaction.RESULTS:
Since the datasets are usually located at or near low-dimensional nonlinear manifolds, we propose an improved GRMF (graph regularized matrix factorization) method to learn these flow patterns in combination with the previous matrix-decomposition method. In addition, we use one of the pre-processing steps previously proposed to improve the accuracy of the prediction.CONCLUSIONS:
Cross-validation is used to evaluate our method, and simulation experiments are used to predict new interactions. In most cases, our method is superior to other methods. Finally, some examples of new drugs and new targets are predicted by performing simulation experiments. And the improved GRMF method can better predict the remaining drug-target interactions.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Interacciones Farmacológicas
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
China