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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Developing an SVM classifier for extended ES protein structure prediction

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DOI: http://dx.doi.org/10.15439/2017F322

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 169172 ()

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Abstract. This article presents a new SVM classifier for the prediction of the extended early-stage (ES) protein structures. The classifier is based on physicochemical features and position-specific scoring matrix (PSSM). Experiments have shown that prediction results for specific classes are significantly better than those already obtained.

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