ArticleFrontiers in bioengineering and biotechnology2020
A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 41 citations in OpenAlex.
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- Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders.Frontiers in pharmacology · 2025Article
- SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.Journal of cellular and molecular medicine · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
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- Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE.iScience · 2022Article
- DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.Computers & chemical engineering · 2022Article
- Research progress on detection techniques for point-of-care testing of foodborne pathogens.Frontiers in bioengineering and biotechnology · 2022Review
- Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data.Frontiers in genetics · 2022Article
- Gastrodin ameliorates the lipopolysaccharide-induced neuroinflammation in mice by downregulating miR-107-3p.Frontiers in pharmacology · 2022Article
- Identifying potential microRNA biomarkers for colon cancer and colorectal cancer through bound nuclear norm regularization.Frontiers in genetics · 2022Article
- MicroRNAs in extracellular vesicles: Sorting mechanisms, diagnostic value, isolation, and detection technology.Frontiers in bioengineering and biotechnology · 2022Review
- WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19.Frontiers in microbiology · 2022Article
- ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations.BMC bioinformatics · 2021Article
- Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study.Applied soft computing · 2021Article
- Evaluation of the MGISEQ-2000 Sequencing Platform for Illumina Target Capture Sequencing Libraries.Frontiers in genetics · 2021Article
- Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model's average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.
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