| Autor: |
Ying Li, Lizheng Wei, Cankun Wang, Jianing Zhao, Siyu Han, Yu Zhang, Wei Du |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-15 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2105 |
| DOI: |
10.1186/s12859-022-04665-3 |
| Popis: |
Abstract 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 . |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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