A hybrid deep learning model for classification of plant transcription factor proteins

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dc.authoridOncul, Ali Burak/0000-0001-9612-1787
dc.authoridCELIK, YUKSEL/0000-0002-7117-9736
dc.contributor.authorOncul, Ali Burak
dc.contributor.authorCelik, Yuksel
dc.date.accessioned2024-09-29T15:54:33Z
dc.date.available2024-09-29T15:54:33Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractStudies on the amino acid sequences, protein structure, and the relationships of amino acids are still a large and challenging problem in biology. Although bioinformatics studies have progressed in solving these problems, the relationship between amino acids and determining the type of protein formed by amino acids are still a problem that has not been fully solved. This problem is why the use of some of the available protein sequences is also limited. This study proposes a hybrid deep learning model to classify amino acid sequences of unknown species using the amino acid sequences in the plant transcription factor database. The model achieved 98.23% success rate in the tests performed. With the hybrid model created, transcription factor proteins in the plant kingdom can be easily classified. The fact that the model is hybrid has made its layers lighter. The training period has decreased, and the success has increased. When tested with a bidirectional LSTM produced with a similar dataset to our dataset and a ResNet-based ProtCNN model, a CNN model, the proposed model was more successful. In addition, we found that the hybrid model we designed by creating vectors with Word2Vec is more successful than other LSTM or CNN-based models. With the model we have prepared, other proteins, especially transcription factor proteins, will be classified, thus enabling species identification to be carried out efficiently and successfully. The use of such a triplet hybrid structure in classifying plant transcription factors stands out as an innovation brought to the literature.en_US
dc.identifier.doi10.1007/s11760-022-02419-5
dc.identifier.endpage2061en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85143236550en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2055en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-022-02419-5
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4137
dc.identifier.volume17en_US
dc.identifier.wosWOS:000914659500002en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectProtein classificationen_US
dc.subjectDeep learningen_US
dc.subjectGRUen_US
dc.subjectCNNen_US
dc.subjectHybrid modelsen_US
dc.subjectWord2Vecen_US
dc.titleA hybrid deep learning model for classification of plant transcription factor proteinsen_US
dc.typeArticleen_US

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