ArticleBioinformatics (Oxford, England)2019
Large-scale inference of competing endogenous RNA networks with sparse partial correlation.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Detection of Candidate Circular RNAs to Monitor Anti-Hormonal Response in the Mammary Gland.bioRxiv : the preprint server for biology · 2026Article
- NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.Biostatistics (Oxford, England) · 2026Article
- Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis.BMC bioinformatics · 2025Article
- Identification of a circRNA-miRNA-mRNA interactome associated with Parkinson's disease progression.Journal of Parkinson's disease · 2025Article
- Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes.BioData mining · 2024Article
- Cirscan: a shiny application to identify differentially active sponge mechanisms and visualize circRNA-miRNA-mRNA networks.BMC bioinformatics · 2024Article
- Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers.PLoS computational biology · 2023Article
- Multi-omics regulatory network inference in the presence of missing data.Briefings in bioinformatics · 2023Article
- circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging.bioRxiv : the preprint server for biology · 2023Article
- spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.Bioinformatics (Oxford, England) · 2023Article
- Integrative competing endogenous RNA network analyses identify novel lncRNA and genes implicated in metastatic breast cancer.Scientific reports · 2023Article
- circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging.Bioinformatics advances · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning.Cancers · 2022Article
- ceRNAR: An R package for identification and analysis of ceRNA-miRNA triplets.PLoS computational biology · 2022Article
- A supervised machine learning approach identifies gene-regulating factor-mediated competing endogenous RNA networks in hormone-dependent cancers.Journal of cellular biochemistry · 2022Article
- MIO: microRNA target analysis system for immuno-oncology.Bioinformatics (Oxford, England) · 2022Article
- mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis.BMC genomics · 2022Article
- Variation in the co-expression profile highlights a loss of miRNA-mRNA regulation in multiple cancer types.Non-coding RNA research · 2022Article
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4 authors.
Funding
Abstract
motivationMicroRNAs (miRNAs) are important non-coding post-transcriptional regulators that are involved in many biological processes and human diseases. Individual miRNAs may regulate hundreds of genes, giving rise to a complex gene regulatory network in which transcripts carrying miRNA binding sites act as competing endogenous RNAs (ceRNAs). Several methods for the analysis of ceRNA interactions exist, but these do often not adjust for statistical confounders or address the problem that more than one miRNA interacts with a target transcript.
resultsWe present SPONGE, a method for the fast construction of ceRNA networks. SPONGE uses 'multiple sensitivity correlation', a newly defined measure for which we can estimate a distribution under a null hypothesis. SPONGE can accurately quantify the contribution of multiple miRNAs to a ceRNA interaction with a probabilistic model that addresses previously neglected confounding factors and allows fast P-value calculation, thus outperforming existing approaches. We applied SPONGE to paired miRNA and gene expression data from The Cancer Genome Atlas for studying global effects of miRNA-mediated cross-talk. Our results highlight already established and novel protein-coding and non-coding ceRNAs which could serve as biomarkers in cancer. AVAILABILITY AND IMPLEMENTATION: SPONGE is available as an R/Bioconductor package (doi: 10.18129/B9.bioc.SPONGE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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