ArticleBioinformatics (Oxford, England)2013
Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 38 citations in OpenAlex.
- Cellular-state control using ribozyme-scaffolded miRNA-sensing and CRISPR-mediated actuation.Cell reports methods · 2026Article
- Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.Briefings in bioinformatics · 2021Article
- miR-489-3p overexpression inhibits lipopolysaccharide-induced nucleus pulposus cell apoptosis, inflammation and extracellular matrix degradation via targeting Toll-like receptor 4.Experimental and therapeutic medicine · 2021Article
- miR-122-5p downregulation attenuates lipopolysaccharide-induced acute lung injury by targeting IL1RN.Experimental and therapeutic medicine · 2021Article
- Circulating microRNA trafficking and regulation: computational principles and practice.Briefings in bioinformatics · 2020Review
- MicroRNA-182-5p relieves murine allergic rhinitis via TLR4/NF-κB pathway.Open medicine (Warsaw, Poland) · 2020Article
- A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis.Frontiers in genetics · 2019Article
- Joint Detection of Associations between DNA Methylation and Gene Expression from Multiple Cancers.IEEE journal of biomedical and health informatics · 2018Article
- Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer.Nucleic acids research · 2018Article
- A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis.BioData mining · 2018Article
- Dynamic and Modularized MicroRNA Regulation and Its Implication in Human Cancers.Scientific reports · 2017Article
- A STATISTICAL FRAMEWORK FOR DATA INTEGRATION THROUGH GRAPHICAL MODELS WITH APPLICATION TO CANCER GENOMICS.The annals of applied statistics · 2017Article
- Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering.Scientific reports · 2017Article
- Joint sparse canonical correlation analysis for detecting differential imaging genetics modules.Bioinformatics (Oxford, England) · 2016Article
- Prioritizing cancer-related microRNAs by integrating microRNA and mRNA datasets.Scientific reports · 2016Article
- Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods.Cancer informatics · 2016Review
- A Meta-Path-Based Prediction Method for Human miRNA-Target Association.BioMed research international · 2016Article
- Potential microRNA-mediated oncogenic intercellular communication revealed by pan-cancer analysis.Scientific reports · 2014Article
- Regression analysis of combined gene expression regulation in acute myeloid leukemia.PLoS computational biology · 2014Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationMicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA-gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power. However, such simple aggregation analysis results in identifying miRNA-gene interactions that are mostly common across datasets, whereas specific interactions may be missed by these methods. Recent releases of The Cancer Genome Atlas data provide paired expression profiling of miRNAs and genes in multiple tumors with sufficiently large sample size. To study both common and cancer-specific interactions, it is desirable to develop a method that can jointly analyze multiple cancers to study miRNA-gene interactions without combining all the data into one single dataset.
resultsWe developed a novel statistical method to jointly analyze expression profiles from multiple cancers to identify miRNA-gene interactions that are both common across cancers and specific to certain cancers. The benefit of this joint analysis approach is demonstrated by both simulation studies and real data analysis of The Cancer Genome Atlas datasets. Compared with simple aggregate analysis or single sample analysis, our method can effectively use the shared information among different but related cancers to improve the identification of miRNA-gene interactions. Another useful property of our method is that it can estimate similarity among cancers through their shared miRNA-gene interactions. AVAILABILITY AND IMPLEMENTATION: The program, MCMG, implemented in R is available at http://bioinformatics.med.yale.edu/group/.
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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.