Publication: DCDA: CircRNA–Disease association prediction with feed-forward neural network and deep autoencoder
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Date
2023-01-01
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Abstract
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: [Figure not available: see fulltext.].
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Tıp, Bilgisayar Bilimleri, Yaşam Bilimleri, Moleküler Biyoloji ve Genetik, Sitogenetik, Sağlık Bilimleri, Temel Tıp Bilimleri, Biyoistatistik ve Tıp Bilişimi, Temel Bilimler, Mühendislik ve Teknoloji, Medicine, Computer Sciences, Life Sciences, Molecular Biology and Genetics, Cytogenetic, Health Sciences, Fundamental Medical Sciences, Biostatistics and Medical Informatics, Natural Sciences, Engineering and Technology, Klinik Tıp (MED), Mühendislik, Bilişim ve Teknoloji (ENG), Yaşam Bilimleri (LIFE), Klinik Tıp, Bilgisayar Bilimi, TIBBİ BİLİŞİM, BİYOKİMYA VE MOLEKÜLER BİYOLOJİ, Clinical Medicine (MED), Engineering, Computing & Technology (ENG), Life Sciences (LIFE), CLINICAL MEDICINE, COMPUTER SCIENCE, MOLECULAR BIOLOGY & GENETICS, MEDICAL INFORMATICS, BIOCHEMISTRY & MOLECULAR BIOLOGY, Genel Biyokimya, Genetik ve Moleküler Biyoloji, Bilgisayar Bilimi Uygulamaları, Fizik Bilimleri, Tıbbi Bilişim, General Biochemistry, Genetics and Molecular Biology, Computer Science Applications, Physical Sciences, Health Informatics, Autoencoder, CircRNA, CircRNA–disease association, Deep learning, Neural network
Citation
Turgut H., TURANLI B., BOZ B., "DCDA: CircRNA–Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder", Interdisciplinary Sciences – Computational Life Sciences, 2023