ArticlePLoS computational biology2017
LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers.
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Who cites it
121 citing papers in PubMed, 235 citations in OpenAlex.
- Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting.Scientific reports · 2025Article
- Prediction of miRNA-disease association based on multisource inductive matrix completion.Scientific reports · 2024Article
- GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer.Clinical and experimental medicine · 2024Article
- Hessian RegularizedInterdisciplinary sciences, computational life sciences · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE.iScience · 2022Article
- Predicting miRNA-disease associations based on graph attention network with multi-source information.BMC bioinformatics · 2022Article
- DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.Interdisciplinary sciences, computational life sciences · 2022Article
- Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism.Biomolecules · 2022Article
- WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19.Frontiers in microbiology · 2022Article
- Editorial: Machine learning-based methods for RNA data analysis-Volume II.Frontiers in genetics · 2022Article
- RFLMDA: A Novel Reinforcement Learning-Based Computational Model for Human MicroRNA-Disease Association Prediction.Biomolecules · 2021Article
- RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method.Interdisciplinary sciences, computational life sciences · 2021Article
- Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations.BMC bioinformatics · 2021Article
- Circular RNAs and complex diseases: from experimental results to computational models.Briefings in bioinformatics · 2021Review
- Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities.Scientific reports · 2021Article
- SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost.BMC bioinformatics · 2021Article
- MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.BMC medical informatics and decision making · 2021Article
- Systematics for types and effects of RNA variations.RNA biology · 2021Article
- Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.BioMed research international · 2021Article
61 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs' potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases' statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model's superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction.
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