ArticleBMC bioinformatics2019
A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Comprehensive review of Bayesian network applications in gastrointestinal cancers.World journal of clinical oncology · 2025Review
- A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network.Frontiers in genetics · 2025Article
- Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks.BMC bioinformatics · 2024Article
- Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.Frontiers in genetics · 2024Article
- lncRNA-disease association prediction based on the weight matrix and projection score.PloS one · 2023Article
- lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.Scientific reports · 2022Article
- gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.BMC bioinformatics · 2022Article
- LncRNA functional annotation with improved false discovery rate achieved by disease associations.Computational and structural biotechnology journal · 2022Article
- 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
- RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation.Molecular genetics and genomics : MGG · 2021Review
- A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs.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
- Heterogeneous graph inference based on similarity network fusion for predicting lncRNA-miRNA interaction.RSC advances · 2020Article
- Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network.Frontiers in cell and developmental biology · 2020Article
- Multiview Consensus Graph Learning for lncRNA-Disease Association Prediction.Frontiers in genetics · 2020Article
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5 authors.
Funding
Abstract
backgroundSince the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, considering the fact that biological experiments are very expensive and time-consuming, it is important to develop efficient computational models to discover potential lncRNA-disease associations.
resultsIn this manuscript, a novel Collaborative Filtering model called CFNBC for inferring potential lncRNA-disease associations is proposed based on Naïve Bayesian Classifier. In CFNBC, an original lncRNA-miRNA-disease tripartite network is constructed first by integrating known miRNA-lncRNA associations, miRNA-disease associations and lncRNA-disease associations, and then, an updated lncRNA-miRNA-disease tripartite network is further constructed through applying the item-based collaborative filtering algorithm on the original tripartite network. Finally, based on the updated tripartite network, a novel approach based on the Naïve Bayesian Classifier is proposed to predict potential associations between lncRNAs and diseases. The novelty of CFNBC lies in the construction of the updated lncRNA-miRNA-disease tripartite network and the introduction of the item-based collaborative filtering algorithm and Naïve Bayesian Classifier, which guarantee that CFNBC can be applied to predict potential lncRNA-disease associations efficiently without entirely relying on known miRNA-disease associations. Simulation results show that CFNBC can achieve a reliable AUC of 0.8576 in the Leave-One-Out Cross Validation (LOOCV), which is considerably better than previous state-of-the-art results. Moreover, case studies of glioma, colorectal cancer and gastric cancer demonstrate the excellent prediction performance of CFNBC as well.
conclusionsAccording to simulation results, due to the satisfactory prediction performance, CFNBC may be an excellent addition to biomedical researches in the future.
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