Your browser doesn't support javascript.
loading
LPInsider: a webserver for lncRNA-protein interaction extraction from the literature.
Li, Ying; Wei, Lizheng; Wang, Cankun; Zhao, Jianing; Han, Siyu; Zhang, Yu; Du, Wei.
Afiliação
  • Li Y; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Wei L; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Wang C; Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA.
  • Zhao J; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Han S; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Zhang Y; Department of Computer Science, Faculty of Engineering, University of Bristol, Bristol, BS8 1UB, UK.
  • Du W; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China. zy26@jlu.edu.cn.
BMC Bioinformatics ; 23(1): 135, 2022 Apr 15.
Article em En | MEDLINE | ID: mdl-35428172
BACKGROUND: Long non-coding RNA (LncRNA) plays important roles in physiological and pathological processes. Identifying LncRNA-protein interactions (LPIs) is essential to understand the molecular mechanism and infer the functions of lncRNAs. With the overwhelming size of the biomedical literature, extracting LPIs directly from the biomedical literature is essential, promising and challenging. However, there is no webserver of LPIs relationship extraction from literature. RESULTS: LPInsider is developed as the first webserver for extracting LPIs from biomedical literature texts based on multiple text features (semantic word vectors, syntactic structure vectors, distance vectors, and part of speech vectors) and logistic regression. LPInsider allows researchers to extract LPIs by uploading PMID, PMCID, PMID List, or biomedical text. A manually filtered and highly reliable LPI corpus is integrated in LPInsider. The performance of LPInsider is optimal by comprehensive experiment on different combinations of different feature and machine learning models. CONCLUSIONS: LPInsider is an efficient analytical tool for LPIs that helps researchers to enhance their comprehension of lncRNAs from text mining, and also saving their time. In addition, LPInsider is freely accessible from http://www.csbg-jlu.info/LPInsider/ with no login requirement. The source code and LPIs corpus can be downloaded from https://github.com/qiufengdiewu/LPInsider .
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China