ArticleMolecules (Basel, Switzerland)2023
Prediction of miRNA-Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.
Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AMFCL: Predicting miRNA-Disease Associations Through Adaptive Multi-source Modality Fusion and Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- Prediction of miRNA-disease association based on multisource inductive matrix completion.Scientific reports · 2024Article
- Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.Scientific reports · 2024Article
- Article
- DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation.BMC bioinformatics · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Numerous pieces of evidence have indicated that microRNA (miRNA) plays a crucial role in a series of significant biological processes and is closely related to complex disease. However, the traditional biological experimental methods used to verify disease-related miRNAs are inefficient and expensive. Thus, it is necessary to design some excellent approaches to improve efficiency. In this work, a novel method (CFSAEMDA) is proposed for the prediction of unknown miRNA-disease associations (MDAs). Specifically, we first capture the interactive features of miRNA and disease by integrating multi-source information. Then, the stacked autoencoder is applied for obtaining the underlying feature representation. Finally, the modified cascade forest model is employed to complete the final prediction. The experimental results present that the AUC value obtained by our method is 97.67%. The performance of CFSAEMDA is superior to several of the latest methods. In addition, case studies conducted on lung neoplasms, breast neoplasms and hepatocellular carcinoma further show that the CFSAEMDA method may be regarded as a utility approach to infer unknown disease-miRNA relationships.
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Registered trials
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