dc.contributor.author | Ozlem Polat | |
dc.contributor.author | Zümray Dokur | |
dc.date.accessioned | 23.07.201910:49:13 | |
dc.date.accessioned | 2019-07-23T16:37:40Z | |
dc.date.available | 23.07.201910:49:13 | |
dc.date.available | 2019-07-23T16:37:40Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1300-0632 | |
dc.identifier.uri | http://www.trdizin.gov.tr/publication/paper/detail/TWpRNE5EZzFOUT09 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/3480 | |
dc.description.abstract | Protein fold classi cation is an important subject in computational biology and a compelling work from the point of machine learning. To deal with such a challenging problem, in this study, we propose a solution method for the classi cation of protein folds using Grow-and-Learn (GAL) neural network together with one-versus-others (OvO) method. To classify the most common 27 protein folds, 125 dimensional data, constituted by the physicochemical properties of amino acids, are used. The study is conducted on a database including 694 proteins: 311 of these proteins are used for training and 383 of them for testing. Overall, the classi cation system achieves 81.2% fold recognition accuracy on the test set, where most of the proteins have less than 25% sequence identity with the ones used during the training. To portray the capabilities of the GAL network among the other methods, comparisons between a few approaches have also been made, and GAL\'s accuracy is found to be higher than those of the existing methods for protein fold classi cation. | en_US |
dc.description.abstract | Protein fold classi cation is an important subject in computational biology and a compelling work from the point of machine learning. To deal with such a challenging problem, in this study, we propose a solution method for the classi cation of protein folds using Grow-and-Learn (GAL) neural network together with one-versus-others (OvO) method. To classify the most common 27 protein folds, 125 dimensional data, constituted by the physicochemical properties of amino acids, are used. The study is conducted on a database including 694 proteins: 311 of these proteins are used for training and 383 of them for testing. Overall, the classi cation system achieves 81.2% fold recognition accuracy on the test set, where most of the proteins have less than 25% sequence identity with the ones used during the training. To portray the capabilities of the GAL network among the other methods, comparisons between a few approaches have also been made, and GAL\'s accuracy is found to be higher than those of the existing methods for protein fold classi cation. | en_US |
dc.language.iso | eng | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Mühendislik | en_US |
dc.subject | Elektrik ve Elektronik | en_US |
dc.title | Protein fold classi cation with Grow-and-Learn network | en_US |
dc.type | article | en_US |
dc.relation.journal | Turkish Journal of Electrical Engineering and Computer Sciences | en_US |
dc.contributor.department | Sivas Cumhuriyet Üniversitesi | en_US |
dc.identifier.volume | 25 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.endpage | 1196 | en_US |
dc.identifier.startpage | 1184 | en_US |
dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US] |