Your browser doesn't support javascript.
loading
BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.
Zhao, Yi-Xin; Yu, Chang-Qing; Li, Li-Ping; Wang, Deng-Wu; Song, Hui-Fan; Wei, Yu.
Afiliación
  • Zhao YX; School of information Engineering, Xijing University, Xi'an, China.
  • Yu CQ; School of information Engineering, Xijing University, Xi'an, China.
  • Li LP; School of information Engineering, Xijing University, Xi'an, China.
  • Wang DW; College of Grassland and Environment Sciences, Xinjiang Agricultural University, Ürümqi, China.
  • Song HF; School of information Engineering, Xijing University, Xi'an, China.
  • Wei Y; School of information Engineering, Xijing University, Xi'an, China.
Front Genet ; 15: 1399810, 2024.
Article en En | MEDLINE | ID: mdl-38798699
ABSTRACT
Increasing research findings suggest that circular RNA (circRNA) exerts a crucial function in the pathogenesis of complex human diseases by binding to miRNA. Identifying their potential interactions is of paramount importance for the diagnosis and treatment of diseases. However, long cycles, small scales, and time-consuming processes characterize previous biological wet experiments. Consequently, the use of an efficient computational model to forecast the interactions between circRNA and miRNA is gradually becoming mainstream. In this study, we present a new prediction model named BJLD-CMI. The model extracts circRNA sequence features and miRNA sequence features by applying Jaccard and Bert's method and organically integrates them to obtain CMI attribute features, and then uses the graph embedding method Line to extract CMI behavioral features based on the known circRNA-miRNA correlation graph information. And then we predict the potential circRNA-miRNA interactions by fusing the multi-angle feature information such as attribute and behavior through Autoencoder in Autoencoder Networks. BJLD-CMI attained 94.95% and 90.69% of the area under the ROC curve on the CMI-9589 and CMI-9905 datasets. When compared with existing models, the results indicate that BJLD-CMI exhibits the best overall competence. During the case study experiment, we conducted a PubMed literature search to confirm that out of the top 10 predicted CMIs, seven pairs did indeed exist. These results suggest that BJLD-CMI is an effective method for predicting interactions between circRNAs and miRNAs. It provides a valuable candidate for biological wet experiments and can reduce the burden of researchers.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Genet Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Genet Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza