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MHAM-NPI: Predicting ncRNA-protein interactions based on multi-head attention mechanism.
Zhou, Zhecheng; Du, Zhenya; Wei, Jinhang; Zhuo, Linlin; Pan, Shiyao; Fu, Xiangzheng; Lian, Xinze.
Afiliación
  • Zhou Z; Wenzhou University of Technology, Wenzhou, 325000, China.
  • Du Z; Guangzhou Xinhua University, Guangzhou, 510520, China.
  • Wei J; Wenzhou University of Technology, Wenzhou, 325000, China.
  • Zhuo L; Wenzhou University of Technology, Wenzhou, 325000, China; Hunan University, Changsha, 410000, China. Electronic address: zhuoninnin@163.com.
  • Pan S; Wenzhou University of Technology, Wenzhou, 325000, China.
  • Fu X; Hunan University, Changsha, 410000, China. Electronic address: xzfu@must.edu.mo.
  • Lian X; Wenzhou University of Technology, Wenzhou, 325000, China. Electronic address: xinzelian@163.com.
Comput Biol Med ; 163: 107143, 2023 09.
Article en En | MEDLINE | ID: mdl-37339574
ABSTRACT
Non-coding RNA (ncRNA) is a functional RNA molecule that plays a key role in various fundamental biological processes, such as gene regulation. Therefore, studying the connection between ncRNA and proteins holds significant importance in exploring the function of ncRNA. Although many efficient and accurate methods have been developed by modern biological scientists, accurate predictions still pose a major challenge for various issues. In our approach, we utilize a multi-head attention mechanism to merge residual connections, allowing for the automatic learning of ncRNA and protein sequence features. Specifically, the proposed method projects node features into multiple spaces based on multi-head attention mechanism, thereby obtaining different feature interaction patterns in these spaces. By stacking interaction layers, higher-order interaction modes can be derived, while still preserving the initial feature information through the residual connection. This strategy effectively leverages the sequence information of ncRNA and protein, enabling the capture of hidden high-order features. The final experimental results demonstrate the effectiveness of our method, with AUC values of 97.4%, 98.5%, and 94.8% achieved on the NPInter v2.0, RPI807, and RPI488 datasets, respectively. These impressive results solidify our method as a powerful tool for exploring the connection between ncRNAs and proteins. We have uploaded the implementation code on GitHub https//github.com/ZZCrazy00/MHAM-NPI.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteínas / ARN no Traducido Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteínas / ARN no Traducido Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article País de afiliación: China