ArticleGenes2018
A Novel Probability Model for LncRNA⁻Disease Association Prediction Based on the Naïve Bayesian Classifier.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 65 citations in OpenAlex.
- A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network.Frontiers in genetics · 2025Article
- iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations.IET systems biology · 2024Article
- Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer.International journal of molecular sciences · 2023Review
- Association filtering and generative adversarial networks for predicting lncRNA-associated disease.BMC bioinformatics · 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
- lncRNA-disease association prediction based on the weight matrix and projection score.PloS one · 2023Article
- BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network.BMC genomics · 2022Article
- lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.Scientific reports · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network.BMC bioinformatics · 2021Article
- Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.PloS one · 2021Article
- Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview.Frontiers in genetics · 2021Review
- Detecting lncRNA-Cancer Associations by Combining miRNAs, Genes, and Prognosis With Matrix Factorization.Frontiers in genetics · 2021Article
- Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations.Frontiers in genetics · 2021Article
- Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.Computational and mathematical methods in medicine · 2021Article
- 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
- LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores.International journal of molecular sciences · 2020Article
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Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and diseases are still very limited. Recently, computational models have been developed to discover potential associations between lncRNAs and diseases by integrating multiple heterogeneous biological data; this has become a hot topic in biological research. In this article, we constructed a global tripartite network by integrating a variety of biological information including miRNA⁻disease, miRNA⁻lncRNA, and lncRNA⁻disease associations and interactions. Then, we constructed a global quadruple network by appending gene⁻lncRNA interaction, gene⁻disease association, and gene⁻miRNA interaction networks to the global tripartite network. Subsequently, based on these two global networks, a novel approach was proposed based on the naïve Bayesian classifier to predict potential lncRNA⁻disease associations (NBCLDA). Comparing with the state-of-the-art methods, our new method does not entirely rely on known lncRNA⁻disease associations, and can achieve a reliable performance with effective area under ROC curve (AUCs)in leave-one-out cross validation. Moreover, in order to further estimate the performance of NBCLDA, case studies of colorectal cancer, prostate cancer, and glioma were implemented in this paper, and the simulation results demonstrated that NBCLDA can be an excellent tool for biomedical research in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.