ArticleFrontiers in genetics2020
LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.PLoS computational biology · 2024Article
- Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes.BioData mining · 2024Article
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.Biometrics · 2022Article
- Multimerin-1 and cancer: a review.Bioscience reports · 2022Review
- Network Approaches to Study Endogenous RNA Competition and Its Impact on Tissue-Specific microRNA Functions.Biomolecules · 2022Review
- ceRNAshiny: An Interactive R/Shiny App for Identification and Analysis of ceRNA Regulation.Frontiers in molecular biosciences · 2022Article
- miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data.RNA biology · 2021Article
- CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network.BMC bioinformatics · 2021Article
- Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.PloS one · 2021Article
- ceRNA network development and tumour-infiltrating immune cell analysis of metastatic breast cancer to bone.Journal of bone oncology · 2020Article
- Identification of Potential Prognostic Competing Triplets in High-Grade Serous Ovarian Cancer.Frontiers in genetics · 2020Article
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3 authors.
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Abstract
Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.
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