A decision support system based on multi-sources information to predict piRNA–disease associations using stacked autoencoder

Despite experimental efforts to map out the human interactome, the continued high data complexity, heterogeneity, and combined explosion limit our ability to understand the molecular roots of human disease. Computational tools provide a promising alternative when identifying biologically significant...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 26; no. 20; pp. 11007 - 11016
Main Authors: Zheng, Kai, Liang, Ying, Liu, Yue-Ying, Yasir, Muhammad, Wang, Ping
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022
Springer Nature B.V
Subjects:
Artificial Intelligence
Breast cancer
Computational Intelligence
Control
Datasets
Decision support systems
Disease
DNA methylation
Engineering
Focus
Genomes
Heterogeneity
Mathematical Logic and Foundations
Mechatronics
Medical Subject Headings-MeSH
MicroRNAs
Proteins
Robotics
Semantics
Software
Subject heading schemes
piRNA–disease associations
Random forest
Disease
Heterogeneous information
PIWI-interacting RNA
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:Despite experimental efforts to map out the human interactome, the continued high data complexity, heterogeneity, and combined explosion limit our ability to understand the molecular roots of human disease. Computational tools provide a promising alternative when identifying biologically significant, yet unmapped associations between PIWI-interacting RNA (piRNA) and diseases. However, few predictors for large-scale prediction of potential disease-associated piRNAs have been proposed to provide effective candidates for future biomedical research now. In this study, we proposed a new computational model based on multi-source information and stacked autoencoder, called MSRDA, to predict potential piRNA–disease associations on a large scale. In particular, we introduce stacked autoencoders into the field of piRNA–disease association prediction for the first time. In the fivefold cross-validation, the MSRDA achieves the average area under the curve of 0.9184 ± 0.0015. To further evaluate the performance of MSRDA, we compared models without feature denoising and models with different types of feature representations. Moreover, the proposed method is optimal compared to related works. In conclusion, MSRDA is an effective tool that provides new impetus for uncovering the root causes of human disease.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-07396-y