SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stack...

Full description

Saved in:
Bibliographic Details
Published in:Cells (Basel, Switzerland) Switzerland), 2022-12, Vol.11 (24), p.3984
Main Authors: Wang, Shudong, Lin, Boyang, Zhang, Yuanyuan, Qiao, Sibo, Wang, Fuyu, Wu, Wenhao, Ren, Chuanru
Format: Article
Language:English
Subjects:
Algorithms
Analysis
association prediction
Biological markers
Case studies
Cell growth
Collaboration
Computational Biology - methods
Deep learning
Disease
higher-order features
Machine learning
MicroRNA
MicroRNAs
MicroRNAs - genetics
miRNA
Neighborhoods
Neural networks
Neural Networks, Computer
Pancreatic cancer
Polymerase chain reaction
Prevention
Regression analysis
Risk factors
ROC Curve
stacked graph autoencoder
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.
ISSN:2073-4409
2073-4409
DOI:10.3390/cells11243984