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DCDA: CircRNA–Disease association prediction with feed-forward neural network and deep autoencoder

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dc.contributor.authorTURANLI, BESTE
dc.contributor.authorBOZ, BETÜL
dc.contributor.authorsTurgut H., TURANLI B., BOZ B.
dc.date.accessioned2023-11-27T09:00:30Z
dc.date.available2023-11-27T09:00:30Z
dc.date.issued2023-01-01
dc.description.abstractCircular 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.].
dc.identifier.citationTurgut H., TURANLI B., BOZ B., "DCDA: CircRNA–Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder", Interdisciplinary Sciences – Computational Life Sciences, 2023
dc.identifier.doi10.1007/s12539-023-00590-y
dc.identifier.issn1913-2751
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85176735602&origin=inward
dc.identifier.urihttps://hdl.handle.net/11424/295162
dc.language.isoeng
dc.relation.ispartofInterdisciplinary Sciences – Computational Life Sciences
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectTıp
dc.subjectBilgisayar Bilimleri
dc.subjectYaşam Bilimleri
dc.subjectMoleküler Biyoloji ve Genetik
dc.subjectSitogenetik
dc.subjectSağlık Bilimleri
dc.subjectTemel Tıp Bilimleri
dc.subjectBiyoistatistik ve Tıp Bilişimi
dc.subjectTemel Bilimler
dc.subjectMühendislik ve Teknoloji
dc.subjectMedicine
dc.subjectComputer Sciences
dc.subjectLife Sciences
dc.subjectMolecular Biology and Genetics
dc.subjectCytogenetic
dc.subjectHealth Sciences
dc.subjectFundamental Medical Sciences
dc.subjectBiostatistics and Medical Informatics
dc.subjectNatural Sciences
dc.subjectEngineering and Technology
dc.subjectKlinik Tıp (MED)
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectYaşam Bilimleri (LIFE)
dc.subjectKlinik Tıp
dc.subjectBilgisayar Bilimi
dc.subjectTIBBİ BİLİŞİM
dc.subjectBİYOKİMYA VE MOLEKÜLER BİYOLOJİ
dc.subjectClinical Medicine (MED)
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectLife Sciences (LIFE)
dc.subjectCLINICAL MEDICINE
dc.subjectCOMPUTER SCIENCE
dc.subjectMOLECULAR BIOLOGY & GENETICS
dc.subjectMEDICAL INFORMATICS
dc.subjectBIOCHEMISTRY & MOLECULAR BIOLOGY
dc.subjectGenel Biyokimya, Genetik ve Moleküler Biyoloji
dc.subjectBilgisayar Bilimi Uygulamaları
dc.subjectFizik Bilimleri
dc.subjectTıbbi Bilişim
dc.subjectGeneral Biochemistry, Genetics and Molecular Biology
dc.subjectComputer Science Applications
dc.subjectPhysical Sciences
dc.subjectHealth Informatics
dc.subjectAutoencoder
dc.subjectCircRNA
dc.subjectCircRNA–disease association
dc.subjectDeep learning
dc.subjectNeural network
dc.titleDCDA: CircRNA–Disease association prediction with feed-forward neural network and deep autoencoder
dc.typearticle
dspace.entity.typePublication
local.avesis.id11b20c5d-978b-45e2-8209-afda13aaf265
local.indexed.atPUBMED
local.indexed.atSCOPUS
local.indexed.atWOS
relation.isAuthorOfPublication3e8c4f64-93ae-4b42-953c-35c8b07586b3
relation.isAuthorOfPublication6ca0046b-2956-4179-a787-af9c434fe055
relation.isAuthorOfPublication.latestForDiscovery3e8c4f64-93ae-4b42-953c-35c8b07586b3

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