ArticleFrontiers in pharmacology2018
Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.
Article in Frontiers in pharmacology, 2018. 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, 54 citations in OpenAlex.
- Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.Molecular diversity · 2025Review
- MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks.BMC bioinformatics · 2025Article
- sChemNET: a deep learning framework for predicting small molecules targeting microRNA function.Nature communications · 2024Article
- Small molecules targeting microRNAs: new opportunities and challenges in precision cancer therapy.Trends in cancer · 2024Review
- Joint masking and self-supervised strategies for inferring small molecule-miRNA associations.Molecular therapy. Nucleic acids · 2024Article
- MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.BMC bioinformatics · 2024Article
- Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.European journal of medicinal chemistry · 2023Review
- AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion.Cells · 2023Article
- Article
- Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion.Frontiers in pharmacology · 2023Article
- Prediction of small molecule drug-miRNA associations based on GNNs and CNNs.Frontiers in genetics · 2023Article
- Review
- MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations.Biology · 2022Article
- Noncoding RNAs Emerging as Drugs or Drug Targets: Their Chemical Modification, Bio-Conjugation and Intracellular Regulation.Molecules (Basel, Switzerland) · 2022Review
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules.Frontiers in molecular biosciences · 2022Article
- Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning.Frontiers in genetics · 2022Article
- Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders.Pharmaceutics · 2021Article
- SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association.RNA biology · 2020Article
- Insights about multi-targeting and synergistic neuromodulators in Ayurvedic herbs against epilepsy: integrated computational studies on drug-target and protein-protein interaction networks.Scientific reports · 2019Article
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
MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17β-Estradiol and 5-Aza-2'-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.
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.