ArticleBMC bioinformatics2021
SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Dual balanced augmented topological noncoding RNA disease triplet association in heterogeneous graphs.Briefings in bioinformatics · 2025Article
- Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome.Bioengineering (Basel, Switzerland) · 2025Article
- Exploring the Role of microRNAs as Blood Biomarkers in Alzheimer's Disease and Frontotemporal Dementia.International journal of molecular sciences · 2025Review
- Predicting noncoding RNA and disease associations using multigraph contrastive learning.Scientific reports · 2025Article
- ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.Briefings in bioinformatics · 2024Article
- A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations.Scientific reports · 2024Article
- HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.PLoS computational biology · 2024Article
- DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.BMC bioinformatics · 2024Article
- SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.Briefings in bioinformatics · 2024Article
- DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation.BMC bioinformatics · 2024Article
- Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.Frontiers in genetics · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- lncRNA-disease association prediction based on the weight matrix and projection score.PloS one · 2023Article
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- Article
- Predicting potential miRNA-disease associations based on more reliable negative sample selection.BMC bioinformatics · 2022Article
- Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.BMC bioinformatics · 2022Article
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important.
resultsIn this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively.
conclusionThe comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.
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