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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Vydáno v:	Interdisciplinary sciences : computational life sciences Ročník 16; číslo 1; s. 91 - 103
Hlavní autoři:	Turgut, Hacer, Turanli, Beste, Boz, Betül
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.03.2024 Springer Nature B.V
Témata:	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
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Popis
Shrnutí:	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
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1913-2751 1867-1462 1867-1462
DOI:	10.1007/s12539-023-00590-y