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BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.
Wang, Xin-Fei; Yu, Chang-Qing; You, Zhu-Hong; Wang, Yan; Huang, Lan; Qiao, Yan; Wang, Lei; Li, Zheng-Wei.
Afiliação
  • Wang XF; School of Information Engineering, Xijing University, Xi'an, China.
  • Yu CQ; School of Information Engineering, Xijing University, Xi'an, China. xaycq@163.com.
  • You ZH; School of Computer Science, Northwestern Polytechnical University, Xi'an, China. zhuhongyou@nwpu.edu.cn.
  • Wang Y; Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China. wy6868@jlu.edu.cn.
  • Huang L; School of Artificial Intelligence, Jilin University, Changchun, China. wy6868@jlu.edu.cn.
  • Qiao Y; Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
  • Wang L; College of Agriculture and Forestry, Longdong University, Qingyang, China.
  • Li ZW; School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
BMC Bioinformatics ; 25(1): 264, 2024 Aug 10.
Article em En | MEDLINE | ID: mdl-39127625
ABSTRACT
Circular RNA (CircRNA)-microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia Computacional / MicroRNAs / RNA Circular Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia Computacional / MicroRNAs / RNA Circular Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article