ArticleJournal of cheminformatics2018
Inferring potential small molecule-miRNA association based on triple layer heterogeneous network.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- 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
- MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction.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
- DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.International journal of molecular sciences · 2023Article
- Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion.Frontiers in pharmacology · 2023Article
- Article
- Review
- 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
- PPDTS: Predicting potential drug-target interactions based on network similarity.IET systems biology · 2022Article
- Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition.Frontiers in molecular biosciences · 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
- A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path.BMC bioinformatics · 2020Article
- Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.Molecular genetics and genomics : MGG · 2020Article
- MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network.ACS omega · 2020Article
- Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk.Frontiers in bioengineering and biotechnology · 2020Article
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.
Funding
Abstract
Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule-MiRNA association prediction (TLHNSMMA) to uncover potential SM-miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM-miRNA associations and miRNA-disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 ± 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 ± 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM-miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM-miRNA associations.
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.