DCDA: CircRNA–Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder

Circular RNA is a single-stranded RNA with a closed-loop structure. In recent years, academic research has revealed that circular RNAs play critical roles in biological processes and are related to human diseases. The discovery of potential circRNAs as disease biomarkers and drug targets is crucial...

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Bibliographic Details
Published in:Interdisciplinary sciences : computational life sciences Vol. 16; no. 1; pp. 91 - 103
Main Authors: Turgut, Hacer, Turanli, Beste, Boz, Betül
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.03.2024
Springer Nature B.V
Subjects:
Algorithms
Artificial neural networks
Biological activity
Biomarkers
Biomedical and Life Sciences
Circular RNA
Closed loops
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Appl. in Life Sciences
Computer applications
COVID-19
Datasets
Deep learning
Dictionaries
Disease
Experimental methods
Health Sciences
Life Sciences
Machine learning
Mathematical and Computational Physics
Medicine
Methods
MicroRNAs
Neural networks
Original Research Article
Performance prediction
Ribonucleic acid
RNA
Semantics
Statistics for Life Sciences
Theoretical
Theoretical and Computational Chemistry
Therapeutic targets
Vectors (Biology)
Deep learning
CircRNA–disease association
Neural network
Autoencoder
CircRNA
ISSN:1913-2751, 1867-1462, 1867-1462
Online Access:Get full text
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Summary:Circular RNA is a single-stranded RNA with a closed-loop structure. In recent years, academic research has revealed that circular RNAs play critical roles in biological processes and are related to human diseases. The discovery of potential circRNAs as disease biomarkers and drug targets is crucial since it can help diagnose diseases in the early stages and be used to treat people. However, in conventional experimental methods, conducting experiments to detect associations between circular RNAs and diseases is time-consuming and costly. To overcome this problem, various computational methodologies are proposed to extract essential features for both circular RNAs and diseases and predict the associations. Studies showed that computational methods successfully predicted performance and made it possible to detect possible highly related circular RNAs for diseases. This study proposes a deep learning-based circRNA–disease association predictor methodology called DCDA, which uses multiple data sources to create circRNA and disease features and reveal hidden feature codings of a circular RNA–disease pair with a deep autoencoder, then predict the relation score of the pair by a deep neural network. Fivefold cross-validation results on the benchmark dataset showed that our model outperforms state-of-the-art prediction methods in the literature with the AUC score of 0.9794. Graphical abstract
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ISSN:1913-2751
1867-1462
1867-1462
DOI:10.1007/s12539-023-00590-y