ArticleScientific reports2014
Semi-supervised learning for potential human microRNA-disease associations inference.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 187 papers.
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Who cites it
187 citing papers in PubMed.
- MAGMDA: a multi-order adaptive graph-based miRNA-disease association prediction model.BMC bioinformatics · 2026Article
- Uncovering miRNA-Disease Associations Through Graph Based Neural Network Representations.Biomedicines · 2026Article
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- RLNSF-MDA: reliability-guided graph-regularized matrix factorization for immune-related miRNA-disease association prediction.Frontiers in bioinformatics · 2026Article
- miCDER: a context-aware transformer model for joint miRNA-disease entity and multi-level regulatory relation extraction.BMC genomics · 2025Article
- Identifying potential miRNA-disease associations through an accurate matrix completion approach.Briefings in bioinformatics · 2025Article
- Biology-Informed Matrix Factorization: An AI-Driven Framework for Enhanced Drug Repositioning.Biology · 2025Article
- DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network.Biomedicines · 2025Article
- Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations.BMC genomic data · 2025Article
- Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association.Biomedicines · 2025Article
- DeepLCRmiRNA: A Hybrid Neural Network Approach for Identifying Lung Cancer-Associated miRNAs.Current gene therapy · 2025Article
- Integrating multiple microRNA functional similarity networks for improved disease-microRNA association prediction.Biology methods & protocols · 2025Article
- Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs.BMC genomics · 2024Article
- HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction.Briefings in bioinformatics · 2024Article
- MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.Briefings in bioinformatics · 2024Article
- Article
- ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.Briefings in bioinformatics · 2024Article
- Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction.PeerJ. Computer science · 2024Article
- MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.Briefings in bioinformatics · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
127 more citing papers are in PubMed but not listed here.
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs play critical role in the development and progression of various diseases. Predicting potential miRNA-disease associations from vast amount of biological data is an important problem in the biomedical research. Considering the limitations in previous methods, we developed Regularized Least Squares for MiRNA-Disease Association (RLSMDA) to uncover the relationship between diseases and miRNAs. RLSMDA can work for diseases without known related miRNAs. Furthermore, it is a semi-supervised (does not need negative samples) and global method (prioritize associations for all the diseases simultaneously). Based on leave-one-out cross validation, reliable AUC have demonstrated the reliable performance of RLSMDA. We also applied RLSMDA to Hepatocellular cancer and Lung cancer and implemented global prediction for all the diseases simultaneously. As a result, 80% (Hepatocellular cancer) and 84% (Lung cancer) of top 50 predicted miRNAs and 75% of top 20 potential associations based on global prediction have been confirmed by biological experiments. We also applied RLSMDA to diseases without known related miRNAs in golden standard dataset. As a result, in the top 3 potential related miRNA list predicted by RLSMDA for 32 diseases, 34 disease-miRNA associations were successfully confirmed by experiments. It is anticipated that RLSMDA would be a useful bioinformatics resource for biomedical researches.
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