ArticleMolecular therapy. Nucleic acids2019
In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.Molecular diversity · 2025Review
- MicroRNA Guided In Silico Drug Repositioning for Malaria.Acta parasitologica · 2024Article
- Small molecules targeting microRNAs: new opportunities and challenges in precision cancer therapy.Trends in cancer · 2024Review
- miRNA expression in PCOS: unveiling a paradigm shift toward biomarker discovery.Archives of gynecology and obstetrics · 2024Review
- Predict lncRNA-drug associations based on graph neural network.Frontiers in genetics · 2024Article
- Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.European journal of medicinal chemistry · 2023Review
- DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.International journal of molecular sciences · 2023Article
- Review
- Article
- MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations.Biology · 2022Article
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- Review
- Using BERT to identify drug-target interactions from whole PubMed.BMC bioinformatics · 2022Article
- Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.Big data and cognitive computing · 2022Article
- DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.Frontiers in genetics · 2022Article
- SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network.Frontiers in oncology · 2022Article
- Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition.Frontiers in molecular biosciences · 2022Article
- Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders.Pharmaceutics · 2021Article
- Circular RNAs and complex diseases: from experimental results to computational models.Briefings in bioinformatics · 2021Review
- Clinico-Pathological Importance of miR-146a in Lung Cancer.Diagnostics (Basel, Switzerland) · 2021Review
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8 authors.
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Abstract
Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.
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