ArticleBMC bioinformatics2020
Cluster correlation based method for lncRNA-disease association prediction.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Revealing new associations between lncRNAs and diseases through cross attention mechanism and multiple level feature fusion.Scientific reports · 2025Article
- Advancements in genetic research and RNA therapy strategies for amyotrophic lateral sclerosis (ALS): current progress and future prospects.Journal of neurology · 2025Review
- Fusion of multi-source relationships and topology to infer lncRNA-protein interactions.Molecular therapy. Nucleic acids · 2024Article
- Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma.Frontiers in oncology · 2023Article
- Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks.BMC bioinformatics · 2022Article
- Deregulation of ncRNA in Neurodegenerative Disease: Focus on circRNA, lncRNA and miRNA in Amyotrophic Lateral Sclerosis.Frontiers in genetics · 2021Review
- LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.International journal of molecular sciences · 2020Article
- LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting.BMC bioinformatics · 2020Article
- Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis.Digital healthArticle
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIn recent years, increasing evidences have indicated that long non-coding RNAs (lncRNAs) are deeply involved in a wide range of human biological pathways. The mutations and disorders of lncRNAs are closely associated with many human diseases. Therefore, it is of great importance to predict potential associations between lncRNAs and complex diseases for the diagnosis and cure of complex diseases. However, the functional mechanisms of the majority of lncRNAs are still remain unclear. As a result, it remains a great challenge to predict potential associations between lncRNAs and diseases.
resultsHere, we proposed a new method to predict potential lncRNA-disease associations. First, we constructed a bipartite network based on known associations between diseases and lncRNAs/protein coding genes. Then the cluster association scores were calculated to evaluate the strength of the inner relationships between disease clusters and gene clusters. Finally, the gene-disease association scores are defined based on disease-gene cluster association scores and used to measure the strength for potential gene-disease associations.
conclusionsLeave-One Out Cross Validation (LOOCV) and 5-fold cross validation tests were implemented to evaluate the performance of our method. As a result, our method achieved reliable performance in the LOOCV (AUCs of 0.8169 and 0.8410 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), and 5-fold cross validation (AUCs of 0.7573 and 0.8198 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), which were significantly higher than the other three comparative methods. Furthermore, our method is simple and efficient. Only the known gene-disease associations are exploited in a graph manner and further new gene-disease associations can be easily incorporated in our model. The results for melanoma and ovarian cancer have been verified by other researches. The case studies indicated that our method can provide informative clues for further investigation.
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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.