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LION: an integrated R package for effective prediction of ncRNA-protein interaction.
Han, Siyu; Yang, Xiao; Sun, Hang; Yang, Hu; Zhang, Qi; Peng, Cheng; Fang, Wensi; Li, Ying.
Afiliação
  • Han S; College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, in Jilin University, China.
  • Yang X; College of Computer Science and Technology, Jilin University, Changchun, China.
  • Sun H; College of Computer Science and Technology, Jilin University, Changchun, China.
  • Yang H; 964 Hospital of Joint Logistic Support Force of the Chinese People's Liberation Army.
  • Zhang Q; College of Computer Science and Technology, Jilin University, Changchun, China.
  • Peng C; School of Software, Tsinghua University, Beijing, China.
  • Fang W; College of Computer Science and Technology, Jilin University, Changchun, China.
  • Li Y; College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
Brief Bioinform ; 23(6)2022 11 19.
Article em En | MEDLINE | ID: mdl-36155620
ABSTRACT
Understanding ncRNA-protein interaction is of critical importance to unveil ncRNAs' functions. Here, we propose an integrated package LION which comprises a new method for predicting ncRNA/lncRNA-protein interaction as well as a comprehensive strategy to meet the requirement of customisable prediction. Experimental results demonstrate that our method outperforms its competitors on multiple benchmark datasets. LION can also improve the performance of some widely used tools and build adaptable models for species- and tissue-specific prediction. We expect that LION will be a powerful and efficient tool for the prediction and analysis of ncRNA/lncRNA-protein interaction. The R Package LION is available on GitHub at https//github.com/HAN-Siyu/LION/.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article