ArticleMolecular therapy. Nucleic acids2020
DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations.
Article in Molecular therapy. Nucleic acids, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- CSDPCDA: A miRNA-Mediated Cross-Semantic Regulatory Network Framework for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2026Article
- Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2024Article
- GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm.BMC bioinformatics · 2023Article
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.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
- MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to Identify Key miRNAs (microRNAs).Frontiers in genetics · 2022Article
- Inferring human miRNA-disease associations via multiple kernel fusion on GCNII.Frontiers in genetics · 2022Article
- TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction.Molecular therapy. Nucleic acids · 2021Article
- Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction.Biomedicines · 2021Article
- DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding.iScience · 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
- PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction.Frontiers in genetics · 2021Article
- NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information.BMC bioinformatics · 2020Article
- iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.PLoS computational biology · 2020Article
- PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences.BMC bioinformatics · 2020Article
- NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information.Evolutionary bioinformatics online · 2020Article
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6 authors.
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Abstract
MicroRNAs (miRNAs) play a critical role in human diseases. Determining the association between miRNAs and disease contributes to elucidating the pathogenesis of liver diseases and seeking the effective treatment method. Despite great recent advances in the field of the associations between miRNAs and diseases, implementing association verification and recognition efficiently at scale presents serious challenges to biological experimental approaches. Thus, computational methods for predicting miRNA-disease association have become a research hotspot. In this paper, we present a new computational method, named distance-based sequence similarity for miRNA-disease association prediction (DBMDA), that directly learns a mapping from miRNA sequence to a Euclidean space. The notable feature of our approach consists of inferring global similarity from region distances that can be figured by chaos game representation algorithm based on the miRNA sequences. In the 5-fold cross-validation experiment, the area under the curve (AUC) obtained by DBMDA in predicting potential miRNA-disease associations reached 0.9129. To assess the effectiveness of DBMDA more effectively, we compared it with different classifiers and former prediction models. Besides, we constructed two case studies for prostate neoplasms and colon neoplasms. Results show that 39 and 39 out of the top 40 predicted miRNAs were confirmed by other databases, respectively. BDMDA has made new attempts in sequence similarity and achieved excellent results, while at the same time providing a new perspective for predicting the relationship between diseases and miRNAs. The source code and datasets explored in this work are available online from the University of Chinese Academy of Sciences (http://220.171.34.3:81/).
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