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dc.contributor.authorRifaioğlu, Ahmet Süreyya
dc.contributor.authorDoğan, Tunca
dc.contributor.authorMartin, Maria Jesus
dc.contributor.authorÇetin-Atalay, Rengül
dc.contributor.authorAtalay, Volkan
dc.date.accessioned2019-07-04T12:03:04Z
dc.date.available2019-07-04T12:03:04Z
dc.date.issued2019en_US
dc.identifier.citationSureyya Rifaioglu, A., Doğan, T., Jesus Martin, M., Cetin-Atalay, R., Atalay, V. (2019). DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks. Scientific Reports. 9(1), 1-16.en_US
dc.identifier.urihttp://dx.doi.org/10.1038/s41598-019-43708-3
dc.identifier.urihttps://hdl.handle.net/20.500.12508/397
dc.descriptionScience Citation Index Expandeden_US
dc.description.abstractAutomated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in computer vision and natural language processing due to the prevention of overfitting and efficient training. Here, we propose DEEPred, a hierarchical stack of multi-task feed-forward deep neural networks, as a solution to Gene Ontology (GO) based protein function prediction. DEEPred was optimized through rigorous hyper-parameter tests, and benchmarked using three types of protein descriptors, training datasets with varying sizes and GO terms form different levels. Furthermore, in order to explore how training with larger but potentially noisy data would change the performance, electronically made GO annotations were also included in the training process. The overall predictive performance of DEEPred was assessed using CAFA2 and CAFA3 challenge datasets, in comparison with the state-of-the-art protein function prediction methods. Finally, we evaluated selected novel annotations produced by DEEPred with a literature-based case study considering the ‘biofilm formation process’ in Pseudomonas aeruginosa. This study reports that deep learning algorithms have significant potential in protein function prediction; particularly when the source data is large. The neural network architecture of DEEPred can also be applied to the prediction of the other types of ontological associations. The source code and all datasets used in this study are available at: https://github.com/cansyl/DEEPred.en_US
dc.language.isoengen_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionof10.1038/s41598-019-43708-3en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.classificationProteins | Genes | Protein functionsen_US
dc.subject.classificationMultidisciplinary Sciencesen_US
dc.subject.othersequenceen_US
dc.subject.otherannotationen_US
dc.subject.otheralignmenten_US
dc.titleDEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networksen_US
dc.typearticleen_US
dc.relation.journalScientific Reportsen_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0001-6717-4767en_US
dc.contributor.authorID0000-0002-1298-9763en_US
dc.contributor.authorID0000-0001-5454-2815en_US
dc.contributor.authorID0000-0001-7850-0601en_US
dc.identifier.volume9en_US
dc.identifier.issue1en_US
dc.identifier.startpage1en_US
dc.identifier.endpage16en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorRifaioğlu, Ahmet Süreyyaen_US
dc.relation.indexWeb of Science - Scopus - PubMeden_US


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