ArticleBMC systems biology2019
PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach.
Article in BMC systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- CDELDA: A Content-Based Dual-Encoder for Cold-Start lncRNA-Disease Association Prediction.Biomedicines · 2026Article
- Neighborhood-Regularized Matrix Factorization for lncRNA-Disease Association Identification.International journal of molecular sciences · 2025Article
- DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network.Biomedicines · 2025Article
- Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association.Biomedicines · 2025Article
- Integrating BERT pre-training with graph common neighbours for predicting ceRNA interactions.Frontiers in genetics · 2025Article
- ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.Briefings in bioinformatics · 2024Article
- Cross Talk between MicroRNAs and Dengue Virus.The American journal of tropical medicine and hygiene · 2024Review
- Lipid metabolism-related miRNAs with potential diagnostic roles in prostate cancer.Lipids in health and disease · 2023Article
- Identification of novel miRNAs potentially involved in the pathogenesis of adult T-cell leukemia/lymphoma using WGCNA followed by RT-qPCR test of hub genes.Infectious agents and cancer · 2023Article
- CmirC: an integrated database of clustered miRNAs co-localized with copy number variations in cancer.Functional & integrative genomics · 2022Article
- Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.Biomedicines · 2022Article
- MDMF: Predicting miRNA-Disease Association Based on Matrix Factorization with Disease Similarity Constraint.Journal of personalized medicine · 2022Article
- ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations.BMC bioinformatics · 2021Article
- MicroRNA-disease association prediction by matrix tri-factorization.BMC genomics · 2020Article
- Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization.Cells · 2020Article
- Three-Layer Heterogeneous Network Combined With Unbalanced Random Walk for miRNA-Disease Association Prediction.Frontiers in genetics · 2019Article
- Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.Frontiers in genetics · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNumerous experimental results have indicated that microRNAs (miRNAs) play a vital role in biological processes, as well as outbreaks of diseases at the molecular level. Despite their important role in biological processes, knowledge regarding specific functions of miRNAs in the development of human diseases is very limited. While attempting to solve this problem, many computational approaches have been proposed and attracted significant attention. However, most previous approaches suffer from the common problem of being inapplicable to new diseases without any known miRNA-disease associations.
resultsThis paper proposes a novel method for inferring disease-miRNA associations utilizing a machine learning technique called matrix factorization, which is widely used in recommendation systems. In recommendation systems, the goal is to predict rating scores that a user might assign to specific items. By replacing users with miRNAs and items with diseases, we can efficiently predict miRNA-disease associations without seed miRNAs. As a result, our proposed model, called prediction of microRNA-disease association utilizing a matrix completion approach, achieves excellent performance compared to previous approaches with a reliable AUC value of 0.882 by implementing five-fold cross validation.
conclusionsTo the best of our knowledge, the proposed method applies the matrix completion technique to infer miRNA-disease associations and overcome the seed-miRNA problem negatively affects existing computational models.
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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.