ArticleBioinformatics (Oxford, England)2014
Protein-driven inference of miRNA-disease associations.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 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
103 citing papers in PubMed, 205 citations in OpenAlex.
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Uncovering miRNA-Disease Associations Through Graph Based Neural Network Representations.Biomedicines · 2026Article
- DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network.Biomedicines · 2025Article
- Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association.Biomedicines · 2025Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction.Briefings in bioinformatics · 2024Article
- MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.Briefings in bioinformatics · 2024Article
- Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction.PeerJ. Computer science · 2024Article
- RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction.Nucleic acids research · 2023Article
- Review
- Article
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- Article
- MDMF: Predicting miRNA-Disease Association Based on Matrix Factorization with Disease Similarity Constraint.Journal of personalized medicine · 2022Article
- Article
- Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.Frontiers in genetics · 2022Article
- RFLMDA: A Novel Reinforcement Learning-Based Computational Model for Human MicroRNA-Disease Association Prediction.Biomolecules · 2021Article
- RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method.Interdisciplinary sciences, computational life sciences · 2021Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- Comprehensive Transcriptome Analyses in Sea Louse Reveal Novel Delousing Drug Responses Through MicroRNA regulation.Marine biotechnology (New York, N.Y.) · 2021Article
43 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA-disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer.
resultsHere we present miRPD in which miRNA-Protein-Disease associations are explicitly inferred. Besides linking miRNAs to diseases, it directly suggests the underlying proteins involved, which can be used to form hypotheses that can be experimentally tested. The inference of miRNAs and diseases is made by coupling known and predicted miRNA-protein associations with protein-disease associations text mined from the literature. We present scoring schemes that allow us to rank miRNA-disease associations inferred from both curated and predicted miRNA targets by reliability and thereby to create high- and medium-confidence sets of associations. Analyzing these, we find statistically significant enrichment for proteins involved in pathways related to cancer and type I diabetes mellitus, suggesting either a literature bias or a genuine biological trend. We show by example how the associations can be used to extract proteins for disease hypothesis. AVAILABILITY AND IMPLEMENTATION: All datasets, software and a searchable Web site are available at http://mirpd.jensenlab.org.
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.