DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder.
Autoencoder
CircRNA
CircRNA–disease association
Deep learning
Neural network
Journal
Interdisciplinary sciences, computational life sciences
ISSN: 1867-1462
Titre abrégé: Interdiscip Sci
Pays: Germany
ID NLM: 101515919
Informations de publication
Date de publication:
17 Nov 2023
17 Nov 2023
Historique:
received:
20
03
2023
accepted:
15
10
2023
revised:
13
10
2023
medline:
18
11
2023
pubmed:
18
11
2023
entrez:
17
11
2023
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 37978116
doi: 10.1007/s12539-023-00590-y
pii: 10.1007/s12539-023-00590-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. International Association of Scientists in the Interdisciplinary Areas.
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