LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm.
In: Frontiers in genetics, Jg. 10 (2019-04-18), S. 343
Online
academicJournal
Zugriff:
According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA.
Titel: |
LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm.
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Autor/in / Beteiligte Person: | Xie, G ; Wu, C ; Sun, Y ; Fan, Z ; Liu, J |
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Zeitschrift: | Frontiers in genetics, Jg. 10 (2019-04-18), S. 343 |
Veröffentlichung: | Lausanne : Frontiers Research Foundation., 2019 |
Medientyp: | academicJournal |
ISSN: | 1664-8021 (print) |
DOI: | 10.3389/fgene.2019.00343 |
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