ArticleBioinformatics (Oxford, England)2013
Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation.
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 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data.BMC biology · 2024Article
- A review on methods for predicting miRNA-mRNA regulatory modules.Journal of integrative bioinformatics · 2022Review
- MicroRNAs (miRNAs) in Cardiovascular Complications of Rheumatoid Arthritis (RA): What Is New?International journal of molecular sciences · 2022Review
- miRModuleNet: Detecting miRNA-mRNA Regulatory Modules.Frontiers in genetics · 2022Article
- Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.BMC bioinformatics · 2021Article
- Simultaneous learning of individual microRNA-gene interactions and regulatory comodules.BMC bioinformatics · 2021Article
- Tiresias: Context-sensitive Approach to Decipher the Presence and Strength of MicroRNA Regulatory Interactions.Theranostics · 2018Article
- Gene-microRNA network module analysis for ovarian cancer.BMC systems biology · 2016Article
- Correlation analysis of the mRNA and miRNA expression profiles in the nascent synthetic allotetraploid Raphanobrassica.Scientific reports · 2016Article
- Ratio of microRNA-122/155 in isoniazid-induced acute liver injury in mice.Experimental and therapeutic medicine · 2016Article
- A survey of best practices for RNA-seq data analysis.Genome biology · 2016Review
- Identification of miRNA-mRNA regulatory modules by exploring collective group relationships.BMC genomics · 2016Article
- Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods.Cancer informatics · 2016Review
- The assembly of miRNA-mRNA-protein regulatory networks using high-throughput expression data.Bioinformatics (Oxford, England) · 2015Article
- Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer.BMC genomics · 2015Article
- Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks.BMC systems biology · 2015Article
- Connecting rules from paired miRNA and mRNA expression data sets of HCV patients to detect both inverse and positive regulatory relationships.BMC genomics · 2015Article
- Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data.Bioinformatics (Oxford, England) · 2014Article
- Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method.BMC bioinformatics · 2014Article
- MixMir: microRNA motif discovery from gene expression data using mixed linear models.Nucleic acids research · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
motivationMicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. MiRNAs were shown to play an important role in development and disease, and accurately determining the networks regulated by these miRNAs in a specific condition is of great interest. Early work on miRNA target prediction has focused on using static sequence information. More recently, researchers have combined sequence and expression data to identify such targets in various conditions.
resultsWe developed the Protein Interaction-based MicroRNA Modules (PIMiM), a regression-based probabilistic method that integrates sequence, expression and interaction data to identify modules of mRNAs controlled by small sets of miRNAs. We formulate an optimization problem and develop a learning framework to determine the module regulation and membership. Applying PIMiM to cancer data, we show that by adding protein interaction data and modeling cooperative regulation of mRNAs by a small number of miRNAs, PIMiM can accurately identify both miRNA and their targets improving on previous methods. We next used PIMiM to jointly analyze a number of different types of cancers and identified both common and cancer-type-specific miRNA regulators. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.