ArticleJournal of cellular and molecular medicine2018
DRMDA: deep representations-based miRNA-disease association prediction.
Article in Journal of cellular and molecular medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed.
- Computational Method Using Attribute-Aware Message Passing and Graph Convolutional Network for Potential miRNA-Disease Association Prediction.International journal of molecular sciences · 2026Article
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.Communications biology · 2024Article
- iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints.RNA (New York, N.Y.) · 2022Article
- TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network.Molecules (Basel, Switzerland) · 2022Article
- Article
- Learning from low-rank multimodal representations for predicting disease-drug associations.BMC medical informatics and decision making · 2021Article
- Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.BMC medical informatics and decision making · 2021Article
- SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.PLoS computational biology · 2021Article
- DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.Molecular therapy : the journal of the American Society of Gene Therapy · 2021Article
- Systematics for types and effects of RNA variations.RNA biology · 2021Article
- MicroRNA-1323 serves as a biomarker in gestational diabetes mellitus and aggravates high glucose-induced inhibition of trophoblast cell viability by suppressing TP53INP1.Experimental and therapeutic medicine · 2021Article
- Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks.Frontiers in genetics · 2021Article
- Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments.Frontiers in genetics · 2021Article
- SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction.Frontiers in cell and developmental biology · 2021Article
- Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms.Scientific reports · 2020Article
- FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.Molecular genetics and genomics : MGG · 2020Article
- Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions.BMC bioinformatics · 2020Article
- Heterogeneous graph inference based on similarity network fusion for predicting lncRNA-miRNA interaction.RSC advances · 2020Article
- Integrative Construction and Analysis of Molecular Association Network in Human Cells by Fusing Node Attribute and Behavior Information.Molecular therapy. Nucleic acids · 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
No grant is acknowledged in the PubMed record.
Abstract
Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA-disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations-based miRNA-disease association (DRMDA) prediction. The original miRNA-disease association data were extracted from HDMM database. Meanwhile, stacked auto-encoder, greedy layer-wise unsupervised pre-training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave-one-out cross-validation (LOOCV), local LOOCV and fivefold cross-validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA-disease 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.