ArticlePLoS computational biology2018
SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
- NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- HGCMLDA: predicting lncRNA-disease associations using hypergraph contrastive learning and multi-scale attentional feature fusion.Briefings in bioinformatics · 2025Article
- A Network Biology-Guided Investigation of the Long Noncoding RNAs' Role in Glaucoma.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Review of the Different Outcomes Produced by Genetic Knock Out of the Long Non-coding microRNA-host-gene MIR22HGMicroRNA (Shariqah, United Arab Emirates) · 2025Review
- Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.BMC genomics · 2024Article
- LINC01116-dependent upregulation of RNA polymerase I transcription drives oncogenic phenotypes in lung adenocarcinoma.Journal of translational medicine · 2024Article
- Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration.ACS pharmacology & translational science · 2024Review
- Upregulation of the long noncoding RNA GJA9-MYCBP and PVT1 is a potential diagnostic biomarker for acute lymphoblastic leukemia.Cancer reports (Hoboken, N.J.) · 2024Article
- Fusion of multi-source relationships and topology to infer lncRNA-protein interactions.Molecular therapy. Nucleic acids · 2024Article
- Kernel Bayesian nonlinear matrix factorization based on variational inference for human-virus protein-protein interaction prediction.Scientific reports · 2024Article
- Localization is the key to action: regulatory peculiarities of lncRNAs.Frontiers in genetics · 2024Review
- ANRIL: A Long Noncoding RNA in Age-related Diseases.Mini reviews in medicinal chemistry · 2024Review
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- LncRNA-protein interaction prediction with reweighted feature selection.BMC bioinformatics · 2023Article
- Hyperbolic matrix factorization improves prediction of drug-target associations.Scientific reports · 2023Article
- Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets.Functional & integrative genomics · 2023Review
- DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions.Bioinformatics (Oxford, England) · 2023Article
- ncRPI-LGAT: Prediction of ncRNA-protein interactions with line graph attention network framework.Computational and structural biotechnology journal · 2023Article
- LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.Frontiers in genetics · 2023Article
11 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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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/.
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