ArticleRNA biology2020
SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association.
Article in RNA biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 63 citations in OpenAlex.
- Equivariant graph neural network-based accurate and ultra-fast virtual screening of small molecules targeting miRNA-protein complex.Journal of pharmaceutical analysis · 2026Article
- Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.Molecular diversity · 2025Review
- Expression of some circulating microRNAs as predictive biomarkers for prognosis and treatment response in glioblastoma.Scientific reports · 2025Article
- SMTRI: A deep learning-based web service for predicting small molecules that target miRNA-mRNA interactions.Molecular therapy. Nucleic acids · 2024Article
- Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data.Interdisciplinary sciences, computational life sciences · 2024Article
- Predict lncRNA-drug associations based on graph neural network.Frontiers in genetics · 2024Article
- A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug-Target Interaction Prediction.Molecules (Basel, Switzerland) · 2023Article
- Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.European journal of medicinal chemistry · 2023Review
- Forging New Therapeutic Targets: Efforts of Tumor Derived Exosomes to Prepare the Pre-Metastatic Niche for Cancer Cell Dissemination and Dormancy.Biomedicines · 2023Review
- DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.International journal of molecular sciences · 2023Article
- Review
- Prediction of small molecule drug-miRNA associations based on GNNs and CNNs.Frontiers in genetics · 2023Article
- MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations.Biology · 2022Article
- XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set.Scientific reports · 2022Article
- PPDTS: Predicting potential drug-target interactions based on network similarity.IET systems biology · 2022Article
- Machine learning in the development of targeting microRNAs in human disease.Frontiers in genetics · 2022Review
- SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network.Frontiers in oncology · 2022Article
- Circular RNAs and complex diseases: from experimental results to computational models.Briefings in bioinformatics · 2021Review
- MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.Journal of translational medicine · 2021Article
- Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.Molecular genetics and genomics : MGG · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of experimental methods. Considering the shortcomings of the previous models, such as the prediction accuracy of some models is low or some cannot be applied for new SMs (miRNAs), we developed a novel model named Symmetric Nonnegative Matrix Factorization for Small Molecule-MiRNA Association prediction (SNMFSMMA). Different from some models directly applying the integrated similarities, SNMFSMMA first performed matrix decomposition on the integrated similarity matrixes, and calculated the Kronecker product of the new integrated similarity matrixes to obtain the SM-miRNA pair similarity. Further, we applied regularized least square to obtain the mapping function of the SM-miRNA pairs to the associated probabilities by minimizing the objective function. On the basis of Dataset 1 and 2 extracted from SM2miR v1.0 database, we implemented global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold cross-validation to evaluate the prediction performance. Finally, the AUC values obtained by SNMFSMMA in these validation reached 0.9711 (0.8895), 0.9698 (0.8884), 0.8329 (0.7651) and 0.9644 ± 0.0035 (0.8814 ± 0.0033) based on Dataset 1 (Dataset 2), respectively. In the first case study, 5 of the top 10 associations predicted were confirmed. In the second, 7 and 8 of the top 10 predicted miRNAs related with 5-FU and 5-Aza-2'-deoxycytidine were confirmed. These results demonstrated the reliable predictive power of SNMFSMMA.
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