ArticleBMC medical genomics2020
Prediction of circRNA-disease associations based on inductive matrix completion.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- CSDPCDA: A miRNA-Mediated Cross-Semantic Regulatory Network Framework for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2026Article
- The circular RNA landscape: Biogenesis, functions, identification pipelines, and biomedical applications.Non-coding RNA research · 2026Review
- Self-supervised learning on graphs predicts non-coding RNA and disease associations.Scientific reports · 2026Article
- Activation of Hedgehog pathway by circEEF2/miR-625-5p/TRPM2 axis promotes prostate cancer cell proliferation through mitochondrial stress.European journal of histochemistry : EJH · 2024Article
- Prediction of miRNA-disease association based on multisource inductive matrix completion.Scientific reports · 2024Article
- Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.BMC medical informatics and decision making · 2024Article
- Biomarker potential of competing endogenous RNA networks in Polycystic Ovary Syndrome (PCOS).Non-coding RNA research · 2024Review
- Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks.BMC bioinformatics · 2024Article
- MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.Bioinformatics (Oxford, England) · 2023Article
- has_circ_0070512 promotes prostate cancer progression by regulating the miR-338-3p/hedgehog signaling pathway.Cancer science · 2023Article
- Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction.Frontiers in genetics · 2023Article
- CircWalk: a novel approach to predict CircRNA-disease association based on heterogeneous network representation learning.BMC bioinformatics · 2022Article
- Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network.Biomolecules · 2022Article
- MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.BMC bioinformatics · 2022Article
- Bioinformatics Analysis of the MicroRNA-Metabolic Gene Regulatory Network in Neuropathic Pain and Prediction of Corresponding Potential Therapeutics.Journal of molecular neuroscience : MN · 2022Article
- Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data.Frontiers in genetics · 2022Article
- Circular RNA circ_RANBP9 exacerbates polycystic ovary syndrome via microRNA-136-5p/Bioengineered · 2021Article
- CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network.BMC bioinformatics · 2021Article
- Circular RNAs and complex diseases: from experimental results to computational models.Briefings in bioinformatics · 2021Review
- Double matrix completion for circRNA-disease association prediction.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies.
resultsHere, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation.
conclusionAll the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.
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