Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/8819
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dc.rights.licenseBY-NC-ND-
dc.contributor.authorYu L.-
dc.contributor.authorZhang, Yusen-
dc.contributor.authorGutman, Ivan-
dc.contributor.authorShi, Yongtang-
dc.contributor.authorDehmer M.-
dc.date.accessioned2020-09-19T16:46:06Z-
dc.date.available2020-09-19T16:46:06Z-
dc.date.issued2017-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/8819-
dc.description.abstract© 2017 The Author(s). We develop a novel position-feature-based model for protein sequences by employing physicochemical properties of 20 amino acids and the measure of graph energy. The method puts the emphasis on sequence order information and describes local dynamic distributions of sequences, from which one can get a characteristic B-vector. Afterwards, we apply the relative entropy to the sequences representing B-vectors to measure their similarity/dissimilarity. The numerical results obtained in this study show that the proposed methods leads to meaningful results compared with competitors such as Clustal W.-
dc.rightsopenAccess-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.sourceScientific Reports-
dc.titleProtein Sequence Comparison Based on Physicochemical Properties and the Position-Feature Energy Matrix-
dc.typearticle-
dc.identifier.doi10.1038/srep46237-
dc.identifier.scopus2-s2.0-85017395202-
Appears in Collections:Faculty of Science, Kragujevac

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