SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions

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/.

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

“…RWNS fused different biological information related to small molecules and miRNAs. However, it may be improved by integrating more data, for example, functional associations between microRNAs and long non-coding RNAs (Zhang et al, 2018b). More importantly, how to integrate these data is still an ongoing challenge.…”

Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms

“…Recently, one broad theme is that lncRNAs can drive the assembly of RNA-protein complexes by facilitating the regulation of gene expression (Rinn and Chang, 2012;Chen and Yan, 2013;Hentze et al, 2018;Munschauer et al, 2018;Nozawa and Gilbert, 2019). lncRNAs achieve their specific functions by interacting with multiple proteins and thus regulating multiple cellular processes (Zhang et al, 2018c;Pyfrom et al, 2019). Studies reported that lncRNAs can activate post-transcriptional gene regulation, splicing, and translation by binding to proteins (Zhang et al,.…”

“…lncRNAs achieve their specific functions by interacting with multiple proteins and thus regulating multiple cellular processes (Zhang et al, 2018c;Pyfrom et al, 2019). Studies reported that lncRNAs can activate post-transcriptional gene regulation, splicing, and translation by binding to proteins (Zhang et al,. 2018c;Li et al, 2019a) Therefore, identifying possible lncRNA-protein interactions (LPIs) is essential for unraveling lncRNA-related activities (Qian et al, 2018;Zhang et al, 2018c;Zhao et al, 2018c).…”

“…Studies reported that lncRNAs can activate post-transcriptional gene regulation, splicing, and translation by binding to proteins (Zhang et al,. 2018c;Li et al, 2019a) Therefore, identifying possible lncRNA-protein interactions (LPIs) is essential for unraveling lncRNA-related activities (Qian et al, 2018;Zhang et al, 2018c;Zhao et al, 2018c). Wet experiments validated parts of LPIs, but experimental methods remain costly and time-consuming.…”

Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

“…As a powerful complementary tool for biological and clinical experiments, many computational approaches have been developed to effectively predict the lncRNA-disease associations (Zou et al, 2016;Chen et al, 2017;Zhang et al, 2018c;Gong et al, 2019;Yue et al, 2019). Under the assumption that similar diseases are more likely to be associated with functionally similar lncRNAs, Chen et al proposed Laplacian regularized least squares for lncRNA-disease association in terms of a semi-supervised learning framework (Chen and Yan, 2013).…”