Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses

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dc.authorid0000-0003-4941-6309
dc.authorid0000-0003-1933-2550
dc.authorid0000-0003-2337-4673
dc.authorid0000-0002-7097-6143
dc.authorid0000-0001-8004-6700
dc.contributor.authorKoca, Mehmet Buraken_US
dc.contributor.authorNourani, Esmaeilen_US
dc.contributor.authorAbbasoğlu, Ferdaen_US
dc.contributor.authorKaradeniz, İlknuren_US
dc.contributor.authorSevilgen, Fatih Erdoğanen_US
dc.date.accessioned2022-10-24T18:52:14Z
dc.date.available2022-10-24T18:52:14Z
dc.date.issued2022-08-13
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractComputational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of path-ogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationKoca, M. B., Nourani, E., Abbasoğlu, F., Karadeniz, İ. & Sevilgen, F. E. (2022). Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses. Computational Biology and Chemistry, 101, 1-14. doi:10.1016/j.compbiolchem.2022.107755en_US
dc.identifier.doi10.1016/j.compbiolchem.2022.107755
dc.identifier.endpage14
dc.identifier.issn1476-9271
dc.identifier.issn1476-928X
dc.identifier.pmid36037723
dc.identifier.scopus2-s2.0-85136494913
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/5071
dc.identifier.urihttp://dx.doi.org/10.1016/j.compbiolchem.2022.107755
dc.identifier.volume101
dc.identifier.wosWOS:000858873400002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorKaradeniz, İlknuren_US
dc.institutionauthorid0000-0002-7097-6143
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputational Biology and Chemistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGraph convolutional networksen_US
dc.subjectPHI networksen_US
dc.subjectProtein-protein interaction predictionen_US
dc.subjectAmino acidsen_US
dc.subjectComputer virusesen_US
dc.subjectConvolutionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectEmbeddingsen_US
dc.subjectForecastingen_US
dc.subjectGraph neural networksen_US
dc.subjectNumerical methodsen_US
dc.subjectVirusesen_US
dc.subjectAmino acid sequenceen_US
dc.subjectConvolutional networksen_US
dc.subjectGraph convolutional networken_US
dc.subjectHuman proteinsen_US
dc.subjectInteraction predictionen_US
dc.subjectNetwork-baseden_US
dc.subjectPHI networken_US
dc.subjectProtein-protein interactionsen_US
dc.subjectProteinsen_US
dc.subjectBioinformaticsen_US
dc.subjectTwo-hybrid system techniquesen_US
dc.subjectPosition weight matrixen_US
dc.titleGraph convolutional network based virus-human protein-protein interaction prediction for novel virusesen_US
dc.typeArticleen_US

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