ArticleMolecular therapy. Nucleic acids2024
Fusion of multi-source relationships and topology to infer lncRNA-protein interactions.
Article in Molecular therapy. Nucleic acids, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- DSGCNLDA: A Multi-view Learning Model with DualScope Attention for lncRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.BMC bioinformatics · 2025Article
- SE-lncRNAs in Cancer: Classification, Subcellular Localisation, Function and Corresponding TFs.Journal of cellular and molecular medicine · 2024Review
- Long Non-Coding RNA LINC01116 Promotes the Proliferation of Lung Adenocarcinoma by Targeting miR-9-5p/CCNE1 Axis.Journal of cellular and molecular medicine · 2024Article
- The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs.Briefings in bioinformatics · 2024Article
- Artificial intelligence-based evaluation of prognosis in cirrhosis.Journal of translational medicine · 2024Review
- Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based approach.Journal of cellular and molecular medicine · 2024Article
- LncRNA-miRNA interactions prediction based on meta-path similarity and Gaussian kernel similarity.Journal of cellular and molecular medicine · 2024Article
- CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.Briefings in bioinformatics · 2024Article
- Microbe-disease associations prediction by graph regularized non-negative matrix factorization withJournal of cellular and molecular medicine · 2024Article
- Predicting potential microbe-disease associations based on dual branch graph convolutional network.Journal of cellular and molecular medicine · 2024Article
- Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines.Frontiers in pharmacology · 2024Article
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Authors and funding
6 authors.
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Abstract
Long non-coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA's functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classification. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the first known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi-source relationships of lncRNAs and proteins with LPN's topological data. Second, the implemented masking strategy efficiently identifies LPN's key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model's potential in LPI prediction.
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