ArticleScientific reports2017
miRsig: a consensus-based network inference methodology to identify pan-cancer miRNA-miRNA interaction signatures.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- CORTADO: hill climbing optimization for cell-type specific marker gene discovery and clustering accuracy improvement.Bioinformatics advances · 2026Article
- GramSeq-DTA: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information.Biomolecules · 2025Article
- Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection.International journal of molecular sciences · 2025Article
- COFFEE: consensus single cell-type specific inference for gene regulatory networks.Briefings in bioinformatics · 2024Article
- CHAI: consensus clustering through similarity matrix integration for cell-type identification.Briefings in bioinformatics · 2024Article
- Neuron enriched extracellular vesicles' MicroRNA expression profiles as a marker of early life alcohol consumption.Translational psychiatry · 2024Article
- CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.bioRxiv : the preprint server for biology · 2024Article
- COFFEE: Consensus Single Cell-Type Specific Inference for Gene Regulatory Networks.bioRxiv : the preprint server for biology · 2024Article
- Examining indicators of complex network vulnerability across diverse attack scenarios.Scientific reports · 2023Article
- Network pharmacology-based strategy to investigate the effect and mechanism of α-solanine against glioma.BMC complementary medicine and therapies · 2023Article
- Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets.Functional & integrative genomics · 2023Review
- pPeActa biochimica et biophysica Sinica · 2022Article
- Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE.iScience · 2022Article
- DisiMiR: Predicting Pathogenic miRNAs Using Network Influence and miRNA Conservation.Non-coding RNA · 2022Article
- Article
- Article
- Human microRNA similarity in breast cancer.Bioscience reports · 2021Article
- Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection.Cancers · 2021Article
- The Transcriptional Regulatory Network ofMicroorganisms · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Decoding the patterns of miRNA regulation in diseases are important to properly realize its potential in diagnostic, prog- nostic, and therapeutic applications. Only a handful of studies computationally predict possible miRNA-miRNA interactions; hence, such interactions require a thorough investigation to understand their role in disease progression. In this paper, we design a novel computational pipeline to predict the common signature/core sets of miRNA-miRNA interactions for different diseases using network inference algorithms on the miRNA-disease expression profiles; the individual predictions of these algorithms were then merged using a consensus-based approach to predict miRNA-miRNA associations. We next selected the miRNA-miRNA associations across particular diseases to generate the corresponding disease-specific miRNA-interaction networks. Next, graph intersection analysis was performed on these networks for multiple diseases to identify the common signature/core sets of miRNA interactions. We applied this pipeline to identify the common signature of miRNA-miRNA inter- actions for cancers. The identified signatures when validated using a manual literature search from PubMed Central and the PhenomiR database, show strong relevance with the respective cancers, providing an indirect proof of the high accuracy of our methodology. We developed miRsig, an online tool for analysis and visualization of the disease-specific signature/core miRNA-miRNA interactions, available at: http://bnet.egr.vcu.edu/miRsig.
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.