ArticleScientific reports2018
TPGLDA: Novel prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.BMC cancer · 2025Article
- Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Machine Learning and Graph Signal Processing Applied to Healthcare: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism.Scientific reports · 2024Article
- GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA-Disease Associations.ACS omega · 2024Article
- Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network.PLoS computational biology · 2023Article
- LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.Interdisciplinary sciences, computational life sciences · 2023Article
- A lncRNA-disease association prediction model based on the two-step PU learning and fully connected neural networks.Heliyon · 2023Article
- lncRNA-disease association prediction based on the weight matrix and projection score.PloS one · 2023Article
- A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.Frontiers in genetics · 2023Article
- A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective.International journal of molecular sciences · 2022Review
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.Briefings in bioinformatics · 2021Article
- SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec.BMC bioinformatics · 2021Article
- lncRNA-disease association prediction based on latent factor model and projection.Scientific reports · 2021Article
- Double matrix completion for circRNA-disease association prediction.BMC bioinformatics · 2021Article
- DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization.BMC bioinformatics · 2021Article
- IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier.BMC bioinformatics · 2021Article
- A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations.BMC bioinformatics · 2021Article
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4 authors.
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Abstract
Accumulating evidences have indicated that lncRNAs play an important role in various human complex diseases. However, known disease-related lncRNAs are still comparatively small in number, and experimental identification is time-consuming and labor-intensive. Therefore, developing a useful computational method for inferring potential associations between lncRNAs and diseases has become a hot topic, which can significantly help people to explore complex human diseases at the molecular level and effectively advance the quality of disease diagnostics, therapy, prognosis and prevention. In this paper, we propose a novel prediction of lncRNA-disease associations via lncRNA-disease-gene tripartite graph (TPGLDA), which integrates gene-disease associations with lncRNA-disease associations. Compared to previous studies, TPGLDA can be used to better delineate the heterogeneity of coding-non-coding genes-disease association and can effectively identify potential lncRNA-disease associations. After implementing the leave-one-out cross validation, TPGLDA achieves an AUC value of 93.9% which demonstrates its good predictive performance. Moreover, the top 5 predicted rankings of lung cancer, hepatocellular carcinoma and ovarian cancer are manually confirmed by different relevant databases and literatures, affording convincing evidence of the good performance as well as potential value of TPGLDA in identifying potential lncRNA-disease associations. Matlab and R codes of TPGLDA can be found at following: https://github.com/USTC-HIlab/TPGLDA .
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