ArticleJournal of translational medicine2019
MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.
Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 119 citations in OpenAlex.
- Computational Method Using Attribute-Aware Message Passing and Graph Convolutional Network for Potential miRNA-Disease Association Prediction.International journal of molecular sciences · 2026Article
- Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations.BMC genomic data · 2025Article
- Prediction of miRNA-Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.Molecules (Basel, Switzerland) · 2023Article
- Putting the "mi" in omics: discovering miRNA biomarkers for pediatric precision care.Pediatric research · 2023Review
- MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations.Biology · 2022Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.Biomedicines · 2022Article
- Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.Biomedicines · 2022Article
- SMMDA: Predicting miRNA-Disease Associations by Incorporating Multiple Similarity Profiles and a Novel Disease Representation.Biology · 2022Article
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network.BMC bioinformatics · 2022Article
- Predict potential miRNA-disease associations based on bounded nuclear norm regularization.Frontiers in genetics · 2022Article
- Article
- DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.Frontiers in bioengineering and biotechnology · 2022Article
- Inferring human miRNA-disease associations via multiple kernel fusion on GCNII.Frontiers in genetics · 2022Article
- SPCMLMI: A structural perturbation-based matrix completion method to predict lncRNA-miRNA interactions.Frontiers in genetics · 2022Article
- Predicting circRNA-Disease Associations Based on Deep Matrix Factorization with Multi-source Fusion.Interdisciplinary sciences, computational life sciences · 2021Article
- EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network.BMC medical informatics and decision making · 2021Article
- SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.PLoS computational biology · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEmerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease.
methodsIn this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA-disease associations.
resultsThe experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA-disease association databases.
conclusionsThese prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/ .
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