ArticleiScience2019
A Learning-Based Method for LncRNA-Disease Association Identification Combing Similarity Information and Rotation Forest.
Article in iScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- iLDA-SGCN: Identifying Associations Between Age-Related Diseases and Long Non-Coding RNAs Using Dual Graph Convolutional Networks.Aging cell · 2026Article
- Revealing new associations between lncRNAs and diseases through cross attention mechanism and multiple level feature fusion.Scientific reports · 2025Article
- MLWNNR: LncRNA-Disease Association Prediction with Multi-Kernel Learning-Driven Weighted Nuclear Norm Regularization.Interdisciplinary sciences, computational life sciences · 2025Article
- LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.BMC bioinformatics · 2025Article
- Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting.Scientific reports · 2025Article
- Predicting noncoding RNA and disease associations using multigraph contrastive learning.Scientific reports · 2025Article
- Mutational insights andFrontiers in bioinformatics · 2025Article
- A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.Briefings in bioinformatics · 2024Article
- GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.Interdisciplinary sciences, computational life sciences · 2024Article
- Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.Frontiers in genetics · 2024Article
- LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.Briefings in bioinformatics · 2023Article
- Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network.PLoS computational biology · 2023Article
- MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations.Biology · 2022Article
- Heterogeneous graph neural network for lncRNA-disease association prediction.Scientific reports · 2022Article
- Long non-coding RNA Sox2OT promotes coronary microembolization-induced myocardial injury by mediating pyroptosis.ESC heart failure · 2022Article
- MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.BMC bioinformatics · 2022Article
- A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.BMC bioinformatics · 2022Article
- DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph.BMC bioinformatics · 2022Review
- Transcription Factor E2F1 Exacerbates Papillary Thyroid Carcinoma Cell Growth and InvasionAnalytical cellular pathology (Amsterdam) · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
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Authors and funding
5 authors.
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Abstract
Long non-coding RNA (lncRNA) play critical roles in the occurrence and development of various diseases. The determination of the lncRNA-disease associations thus would contribute to provide new insights into the pathogenesis of the disease, the diagnosis, and the gene treatments. Considering that traditional experimental approaches are difficult to detect potential human lncRNA-disease associations from the vast amount of biological data, developing computational method could be of significant value. In this paper, we proposed a novel computational method named LDASR to identify associations between lncRNA and disease by analyzing known lncRNA-disease associations. First, the feature vectors of the lncRNA-disease pairs were obtained by integrating lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. Second, autoencoder neural network was employed to reduce the feature dimension and get the optimal feature subspace from the original feature set. Finally, Rotating Forest was used to carry out prediction of lncRNA-disease association. The proposed method achieves an excellent preference with 0.9502 AUC in leave-one-out cross-validations (LOOCV) and 0.9428 AUC in 5-fold cross-validation, which significantly outperformed previous methods. Moreover, two kinds of case studies on identifying lncRNAs associated with colorectal cancer and glioma further proves the capability of LDASR in identifying novel lncRNA-disease associations. The promising experimental results show that the LDASR can be an excellent addition to the biomedical research in the future.
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