dc.contributor.author | Polat, Ozlem | |
dc.contributor.author | Dokur, Zumray | |
dc.date.accessioned | 2019-07-27T12:10:23Z | |
dc.date.accessioned | 2019-07-28T09:44:21Z | |
dc.date.available | 2019-07-27T12:10:23Z | |
dc.date.available | 2019-07-28T09:44:21Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1300-0632 | |
dc.identifier.issn | 1303-6203 | |
dc.identifier.uri | https://dx.doi.org/10.3906/elk-1506-126 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/7029 | |
dc.description | WOS: 000399461300042 | en_US |
dc.description.abstract | Protein fold classification 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 classification 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 classification 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 classification. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY | en_US |
dc.relation.isversionof | 10.3906/elk-1506-126 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Protein fold classification | en_US |
dc.subject | grow and learn neural network | en_US |
dc.subject | attributes for protein fold recognition | en_US |
dc.subject | bioinformatics | en_US |
dc.title | Protein fold classification 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 | [Polat, Ozlem] Cumhuriyet Univ, Fac Technol, Dept Biomed Engn, Sivas, Turkey -- [Dokur, Zumray] Istanbul Tech Univ, Fac Elect & Elect Engn, Dept Elect & Commun Engn, Istanbul, Turkey | 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 - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |