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RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding.
Yu, Chang-Qing; Wang, Xin-Fei; Li, Li-Ping; You, Zhu-Hong; Ren, Zhong-Hao; Chu, Peng; Guo, Feng; Wang, Zhen-Yu.
Afiliación
  • Yu CQ; School of Information Engineering, Xijing University, Xi'an 710123 China.
  • Wang XF; College of Computer Science and Technology, Jilin University, Changchun 130012 China.
  • Li LP; Yizhi School of Agriculture and Forestry, Xiangyang Polytechnic Institute, Xianyang 712000, China.
  • You ZH; School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.
  • Ren ZH; College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
  • Chu P; School of Information Engineering, Xijing University, Xi'an 710123 China.
  • Guo F; School of Information Engineering, Xijing University, Xi'an 710123 China.
  • Wang ZY; School of Telecommunications, Lanzhou University of Technology, Lanzhou 730000, China.
J Chem Inf Model ; 2024 Sep 04.
Article en En | MEDLINE | ID: mdl-39231016
ABSTRACT
Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https//github.com/1axin/RBNE-CMI.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos