ArticleMolecular therapy. Nucleic acids2019
SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- MLWNNR: LncRNA-Disease Association Prediction with Multi-Kernel Learning-Driven Weighted Nuclear Norm Regularization.Interdisciplinary sciences, computational life sciences · 2025Article
- CRISPR-Cas9: bridging the gap between aging mechanisms and therapeutic advances in neurodegenerative disorders.Frontiers in cellular neuroscience · 2025Review
- Neighborhood based computational approaches for the prediction of lncRNA-disease associations.BMC bioinformatics · 2024Article
- CRISPR/Cas9 genome editing for neurodegenerative diseases.EXCLI journal · 2023Review
- gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.BMC bioinformatics · 2022Article
- Recent advances in machine learning methods for predicting LncRNA and disease associations.Frontiers in cellular and infection microbiology · 2022Review
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations.BMC bioinformatics · 2021Article
- MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.Journal of translational medicine · 2021Article
- Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations.Frontiers in genetics · 2021Article
- Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview.Frontiers in genetics · 2021Review
- Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.BMC bioinformatics · 2020Article
- Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets.Molecular therapy. Nucleic acids · 2020Article
- LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting.BMC bioinformatics · 2020Article
- A random forest based computational model for predicting novel lncRNA-disease associations.BMC bioinformatics · 2020Article
- Multiview Consensus Graph Learning for lncRNA-Disease Association Prediction.Frontiers in genetics · 2020Article
- Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection.Frontiers in bioengineering and biotechnology · 2020Article
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4 authors.
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Abstract
Recently, prediction of lncRNA-disease associations has attracted more and more attentions. Various computational models have been proposed; however, there is still room to improve the prediction accuracy. In this paper, we propose a kernel fusion method with different types of similarities for the lncRNAs and diseases. The expression similarity and cosine similarity are used for lncRNAs, and the semantic similarity and cosine similarity are used for the diseases. To eliminate the noise effect, a neighbor constraint is enforced to refine all the similarity matrices before fusion. Experimental results show that the proposed similarity kernel fusion (SKF)-LDA method has the superiority performance in terms of AUC values and other measurements. In the schemes of LOOCV and 5-fold CV, AUC values of SKF-LDA achieve 0.9049 and 0.8743±0.0050 respectively. In addition, the conducted case studies of three diseases (hepatocellular carcinoma, lung cancer, and prostate cancer) show that SKF-LDA can predict related lncRNAs accurately.
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