Inferring Potential CircRNA–Disease Associations via Deep Autoencoder-Based Classification

Aim Circular RNAs (circRNA) are endogenous non-coding RNA molecules with a stable circular conformation. Growing evidence from recent experiments reveals that dysregulations and abnormal expressions of circRNAs are correlated with complex diseases. Therefore, identifying the causal circRNAs behind d...

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Veröffentlicht in:Molecular diagnosis & therapy Jg. 25; H. 1; S. 87 - 97
Hauptverfasser: Deepthi, K., Jereesh, A. S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.01.2021
Springer Nature B.V
Schlagworte:
Algorithms
Biomedical and Life Sciences
Biomedicine
Cancer Research
Case studies
Classifiers
Computer applications
Conformation
Disease
Diseases
Experiments
Feature extraction
Gene expression
Human Genetics
Laboratory Medicine
Machine learning
Methods
Molecular Medicine
Neural networks
Non-coding RNA
Ontology
Original Research Article
Pathogenesis
Performance prediction
Pharmacotherapy
Semantics
ISSN:1177-1062, 1179-2000, 1179-2000
Online-Zugang:Volltext
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Zusammenfassung:Aim Circular RNAs (circRNA) are endogenous non-coding RNA molecules with a stable circular conformation. Growing evidence from recent experiments reveals that dysregulations and abnormal expressions of circRNAs are correlated with complex diseases. Therefore, identifying the causal circRNAs behind diseases is invaluable in explaining the disease pathogenesis. Since biological experiments are difficult, slow-progressing, and prohibitively expensive, computational approaches are necessary for identifying the relationships between circRNAs and diseases. Methods We propose an ensemble method called AE-RF, based on a deep autoencoder and random forest classifier, to predict potential circRNA–disease associations. The method first integrates circRNA and disease similarities to construct features. The integrated features are sent to the deep autoencoder, to extract hidden biological patterns. With the extracted deep features, the random forest classifier is trained for association prediction. Results and discussion AE-RF achieved AUC scores of 0.9486 and 0.9522, in fivefold and tenfold cross-validation experiments, respectively. We conducted case studies on the top-most predicted results and three common human cancers. We compared the method with state-of-the-art classifiers and related methods. The experimental results and case studies demonstrate the prediction power of the model, and it outperforms previous methods with high degree of robustness. Training the classifier with the unique features retrieved by the autoencoder enhanced the model’s predictive performance. The top predicted circRNAs are promising candidates for further biological tests.
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ISSN:1177-1062
1179-2000
1179-2000
DOI:10.1007/s40291-020-00499-y