Your browser doesn't support javascript.
loading
SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.
Zhang, Wen; Yue, Xiang; Tang, Guifeng; Wu, Wenjian; Huang, Feng; Zhang, Xining.
Afiliação
  • Zhang W; College of Informatics, Huazhong Agricultural University, Wuhan, China.
  • Yue X; School of Computer Science, Wuhan University, Wuhan, China.
  • Tang G; Department of Computer Science and Engineering, The Ohio State University, Columbus, United States of America.
  • Wu W; School of Computer Science, Wuhan University, Wuhan, China.
  • Huang F; Electronic Information School, Wuhan University, Wuhan, China.
  • Zhang X; School of Computer Science, Wuhan University, Wuhan, China.
PLoS Comput Biol ; 14(12): e1006616, 2018 12.
Article em En | MEDLINE | ID: mdl-30533006
ABSTRACT
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don't have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, "SFPEL-LPI", to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http//www.bioinfotech.cn/SFPEL-LPI/.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas de Ligação a RNA / RNA Longo não Codificante / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas de Ligação a RNA / RNA Longo não Codificante / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article